12.1.4 Image and Sensor Fusion -- General

Chapter Contents (Back)
Image Fusion. Sensor Fusion. Fusion.
See also Multiple Cameras or Views.
See also Fusion, General Multi-Modal.
See also Using Wavelets for Detection, Recognition, Fusion.

Durrant-Whyte, H.F.,
Consistent Integration and Propagation of Disparate Sensor Observations,
IJRR(6), No. 3, 1987, pp. 3-24.
See also Sensor Models and Multisensor Integration. BibRef 8700

Muller, M.A.,
Comment on 'Consistent Integration and Propagation of Disparate Sensor Observations',
IJRR(11), 1992, pp. 598-600. BibRef 9200

Tinkler, R.D.[Richard D.],
System and method for fusing video imagery from multiple sources in real time,
US_Patent5,140,416, Augst 18, 1992.
WWW Link. BibRef 9208

Crowley, J.L., and Demazeau, Y.,
Principles and Techniques for Sensor Data Fusion,
SP(32), Nos. 1-2, May 1993, pp. 5-27. BibRef 9305

Toet, A.,
Image Fusion by a Ratio of Low-Pass Pyramid,
PRL(9), 1989, pp. 245-253. BibRef 8900

Toet, A.,
A Morphological Pyramidal Image Decomposition,
PRL(9), 1989, pp. 255-261. BibRef 8900

Toet, A.,
A Hierarchical Morphological Image Decomposition,
PRL(11), 1990, pp. 267-274. BibRef 9000

Toet, A.,
Hierarchical Image Fusion,
MVA(3), 1990, pp. 1-11. BibRef 9000

Toet, A., Walraven, J.,
New False Color Mapping for Image Fusion,
OptEng(35), No. 3, March 1996, pp. 650-658. BibRef 9603

Gadagkar, H.P.[Hrishikesh P.], and Trivedi, M.M.[Mohan M.],
Computational Approaches for Processing and Analysis of Tactile Information,
Advances in Computers(35), 1992, pp. 81-134. Object Recognition. Tactile Sensing. BibRef 9200
Earlier:
Tactile Sensory Analysis for Robotic Applications,
SPIE(1293), Applications of AI VIII, Orlando, FL, April 1990, pp. 788-800. BibRef
And:
Towards Tactile Sensor-Based Exploration in Robotic Environment,
SPIE(1383), Sensor Fusion III: 3-D Perception and Recognition, Boston, MA, November 1990. BibRef

Houzelle, S., Giraudon, G.,
Contribution To Multisensor Fusion Formalization,
RobAS(13), No. 1, 1994, pp. 69-85. BibRef 9400

Coombs, D.[David],
Sensor Fusion in Motion Perception,
BBS(17), No. 2, June 1994, pp. 317-318.
PS File. BibRef 9406

Pien, H.H., Gauch, J.M.,
A Variational Approach to Multisensor Fusion of Images,
AppIntel(5), No. 3, July 1995, pp. 217-235. BibRef 9507

Jain, Z.S., Chau, Y.G.A.,
Optimum Multisensor Data Fusion for Image Change Detection,
SMC(25), No. 9, September 1995, pp. 1340-1347. Change Detection. BibRef 9509

Ansari, N., Hou, E.S.H., Zhu, B.O., Chen, J.G.,
Adaptive Fusion by Reinforcement Learning for Distributed Detection Systems,
AeroSys(32), No. 2, April 1996, pp. 524-531. 9605
BibRef

Brooks, R.R.[Richard R.], Iyengar, S.S.[S. Sitharama],
Robust Distributed Computing and Sensing Algorithm,
Computer(29), No. 6, June 1996, pp. 53-60. 9606
How to determine whether data from a sensor is good or bad, when multiple sensors are available. BibRef

Maheshkumar, J.R., Veeranna, V., Iyengar, S.S., Brooks, R.R.,
New Computational Technique for Complementary Sensor Integration in Detection Localization Systems,
OptEng(35), No. 3, March 1996, pp. 674-684. BibRef 9603

Hughes, K., and Ranganathan, N.,
Modeling Sensor Confidence for Sensor Integration Tasks,
PRAI(8), 1994, pp. 1301-1318. BibRef 9400

Luo, R.C., Lin, M.H., Scherp, R.S.,
Dynamic Multisensor Data Fusion System for Intelligent Robots,
RA(4), 1988, pp. 386-396. BibRef 8800

Gutierrez-Osuna, R., Luo, R.C.,
Lola: Probabilistic Navigation for Topological Maps,
AIMag(17), No. 1, Spring 1995, pp. 55-62. BibRef 9500

Legrand, R., Luo, R.C.,
Lola: Object Manipulation in an Unstructured Environment,
AIMag(17), No. 1, Spring 1995, pp. 63-70. BibRef 9500

Luo, R.C., Lin, M.H.,
Multi-Sensor Integrated Intelligent Robot for Automated Assembly,
SRMSF87(118-127). BibRef 8700

Mintz, M.,
Comments on 'Dynamic Multi-Sensor Data Fusion System for Intelligent Robots',
RA(6), 1990, pp. 104-106.
See also Dynamic Multisensor Data Fusion System for Intelligent Robots. BibRef 9000

Hager, G.D.,
Task-Directed Computation of Quantitative Decisions from Sensor Data,
RA(10), 1994, pp. 415-429. BibRef 9400

Hager, G.D., Mintz, M.,
Computational Methods for Task-Directed Sensor Data Fusion and Planning,
IJRR(10), 1991, pp. 285-313. BibRef 9100

Hager, G.D.[Gregory D.],
Task-Directed Sensor Fusion and Planning: A Computational Approach,
Norwell, MA: KluwerAcademic, May 1990, 272 pp. SBN 0-7923-9108-X. BibRef 9005 Ph.D.Thesis BibRef UPenn BibRef

Abidi, M.A., Gonzalez, R.C.,
The Use of Multisensor Data for Robotic Applications,
RA(6), 1990, pp. 159-177. BibRef 9000

Kinser, J.M.,
Pulse-Coupled Image Fusion,
OptEng(36), No. 3, March 1997, pp. 737-742. 9704
BibRef

Brooks, R.R., Iyengar, S.S.,
Real-Time Distributed Sensor Fusion for Time Critical Sensor Readings,
OptEng(36), No. 3, March 1997, pp. 767-779. 9704
BibRef

Katayama, T.[Tatsushi], Niwa, Y.[Yukichi], Suda, S.[Shigeyuki],
Multi-lens imaging apparatus having a mechanism for combining a plurality of images without displacement of registration,
US_Patent5,668,595, Sep 16, 1997
WWW Link. BibRef 9709

Zhou, Y.F., Leung, H., Bosse, E.,
Registration of Mobile Sensors Using the Parallelized Extended Kalman Filter,
OptEng(36), No. 3, March 1997, pp. 780-788. 9704
BibRef

Lee, S.,
Sensor Fusion and Planning with Perception-Action Network,
JIRS(19), No. 3, July 1997, pp. 271-298. 9709
BibRef

Granrath, D., Lersch, J.,
Fusion of Images on Affine Sampling Grids,
JOSA-A(15), No. 4, April 1998, pp. 791-801. 9804
BibRef

Schetselaar, E.M.,
Fusion by the IHS Transform: Should We Use Cylindrical or Spherical Coordinates,
JRS(19), No. 4, March 10 1998, pp. 759-765. 9803
BibRef

Matia, F., Jimenez, A.,
Multisensor Fusion: An Autonomous Mobile Robot,
JIRS(22), No. 2, June 1998, pp. 129-141. 9807
BibRef

Kim, I., Vachtsevanos, G.J.,
Overlapping Object Recognition: A Paradigm for Multiple Sensor Fusion,
RAMag(5), No. 3, September 1998, pp. 37-44. 9810
BibRef

Shekhar, C.[Chandra], Govindu, V.[Venu], Chellappa, R.[Rama],
Multisensor image registration by feature consensus,
PR(32), No. 1, January 1999, pp. 39-52.
Elsevier DOI BibRef 9901
Earlier:
Image Registration by Feature Consensus,
UMDTR3679, August 1996.
WWW Link. Feature correspondence does not work so use a consensus of possible matches for all features. BibRef

Sankaranarayanan, A.C.[Aswin C.], Chellappa, R.[Rama],
Stochastic fusion of multi-view gradient fields,
ICIP08(1324-1327).
IEEE DOI 0810
BibRef

Jouseau, E., Dorizzi, B.,
Neural networks and fuzzy data fusion. Application to an on-line and real time vehicle detection system,
PRL(20), No. 1, January 1999, pp. 97-107. BibRef 9901

Joshi, R., Sanderson, A.C.,
Minimal Representation Multisensor Fusion Using Differential Evolution,
SMC-A(29), No. 1, January 1999, pp. 63.
IEEE Top Reference. BibRef 9901

Wan, W., Fraser, D.,
Multisource Data Fusion with Multiple Self-Organizing Maps,
GeoRS(37), No. 3, May 1999, pp. 1344.
IEEE Top Reference. BibRef 9905

Zhukov, B.S., Oertel, D.A., Lanzl, F., Reinhäckel, G.,
Unmixing-Based Multisensor Multiresolution Image Fusion,
GeoRS(37), No. 3, May 1999, pp. 1212.
IEEE Top Reference. BibRef 9905

Asif, A., Moura, J.M.F.,
Data Assimilation in Large Time-Varying Multidimensional Fields,
IP(8), No. 11, November 1999, pp. 1593-1607.
IEEE DOI 9911
BibRef

Asif, A.,
Fast Implementations of the Kalman-Bucy Filter for Satellite Data Assimilation,
SPLetters(11), No. 2, February 2004, pp. 235-238.
IEEE Abstract. 0402
BibRef

Argenti, F., Alparone, L.,
Filterbanks Design for Multisensor Data Fusion,
SPLetters(7), No. 5, May 2000, pp. 100-103.
IEEE Top Reference. 0005
BibRef

Liu, J.G.,
Smoothing Filter-based Intensity Modulation: a spectral preserve image fusion technique for improving spatial details,
JRS(21), No. 18, December 2000, pp. 3461-3472. 0102
BibRef

Bogoni, L.[Luca], Hansen, M.[Michael],
Pattern-selective color image fusion,
PR(34), No. 8, August 2001, pp. 1515-1526.
Elsevier DOI 0105
pattern-selective color image fusion. Apply to dynamic range, depth of focus. BibRef

Bogoni, L.[Luca], Hansen, M.[Michael], Burt, P.J.,
Image enhancement using pattern-selective color image fusion,
CIAP99(44-49).
IEEE DOI 9909
BibRef

Pan, H.[Hao], Liang, Z.P.[Zhi-Pei], Huang, T.S.[Thomas S.],
Estimation of the joint probability of multisensory signals,
PRL(22), No. 13, November 2001, pp. 1431-1437.
Elsevier DOI 0108
BibRef

Nikou, C.[Christophoros], Heitz, F.[Fabrice], Armspach, J.P.[Jean-Paul],
Robust voxel similarity metrics for the registration of dissimilar single and multimodal images,
PR(32), No. 8, August 1999, pp. 1351-1368.
Elsevier DOI BibRef 9908
Earlier:
Robust Registration of Dissimilar Single and Multi-Modal Images,
ECCV98(II: 51).
Springer DOI BibRef
Earlier:
Multimodal image registration using statistically constrained deformable multimodels,
ICIP98(I: 838-842).
IEEE DOI 9810
Subpixel registration.
See also efficient incremental strategy for constrained groupwise registration based on symmetric pairwise registration, An. BibRef

Scheunders, P.[Paul], de Backer, S.[Steve],
Fusion and merging of multispectral images with use of multiscale fundamental forms,
JOSA-A(18), No. 10, October 2001, pp. 2468-2477.
WWW Link. 0201
BibRef
Earlier:
Multispectral Image Fusion and Merging Using Multiscale Fundamental Forms,
ICIP01(I: 902-905).
IEEE DOI 0108

See also multivalued image wavelet representation based on multiscale fundamental forms, A. BibRef

Tapiador, F.J., Casanova, J.L., Hlavka, C.A., Dungan, J.L.,
An algorithm for the fusion of images based on Jaynes' maximum entropy method,
JRS(23), No. 4, February 2002, pp. 777-785. 0202
BibRef

Socolinsky, D.A.[Diego A.], Wolff, L.B.[Lawrence B.],
Multispectral image visualization through first-order fusion,
IP(11), No. 8, August 2002, pp. 923-931.
IEEE DOI 0209
BibRef
Earlier:
A New Visualization Paradigm for Multispectral Imagery and Data Fusion,
CVPR99(I: 319-324).
IEEE DOI BibRef
Earlier:
Optimal Grayscale Visualization of Local Contrast in Multispectral Imagery,
DARPA98(761-766). First order contrast. Improve results for user viewing the images. BibRef

Hermosillo, G.[Gerardo], Chef d'Hotel, C.[Christophe], Faugeras, O.D.[Olivier D.],
Variational Methods for Multimodal Image Matching,
IJCV(50), No. 3, December 2002, pp. 329-343.
DOI Link 0211
BibRef

Chef d'Hotel, C.[Christophe], Hermosillo, G.[Gerardo], Faugeras, O.D.[Olivier D.],
A Variational Approach to Multi-Modal Image Matching,
LevelSet01(xx-yy). 0106
BibRef
And: A2, A1, A3: INRIARR-4117, February 2001.
HTML Version. Non-parametric image matching. Show various examples. 0105
BibRef

Chef d'Hotel, C.[Christophe],
A Method for the Transport and Registration of Images on Implicit Surfaces,
SSVM07(860-870).
Springer DOI 0705
BibRef

Hermosillo, G., Faugeras, O.D.,
Dense Image Matching with Global and Local Statistical Criteria: A Variational Approach,
CVPR01(I:73-78).
IEEE DOI 0110
Award, CVPR, Student, HM. Image registration. BibRef

Faugeras, O.D.[Olivier D.], Hermosillo, G.[Gerardo],
Well-posedness of eight problems of multi-modal statistical image-matching,
INRIARR-4235, August 2001.
HTML Version. 0211
BibRef

Liu, J.D.[Jun-Dong], Vemuri, B.C.[Baba C.], Bova, F.[Frank],
Efficient multi-modal image registration using local-frequency maps,
MVA(13), No. 3, 2002, pp. 149-163.
Springer DOI 0208
BibRef
Earlier:
Multimodal Image Registration Using Local Frequency,
WACV00(120-125).
IEEE DOI 0010
Registration using features. BibRef

Liu, J.D.[Jun-Dong], Vemuri, B.C.[Baba C.], Marroquin, J.L.,
Local frequency representations for robust multimodal image registration,
MedImg(21), No. 5, May 2002, pp. 462-469.
IEEE Top Reference. 0206
BibRef

Wang, F., Vemuri, B.C., Rao, M., Chen, Y.,
Cumulative residual entropy, a new measure of information and its application to image alignment,
ICCV03(548-553).
IEEE DOI 0311
BibRef

Fabre, S., Briottet, X., Appriou, A.,
Impact of contextual information integration on pixel fusion,
GeoRS(40), No. 9, September 2002, pp. 1997-2010.
IEEE Top Reference. 0212
BibRef

Aiazzi, B., Alparone, L., Baronti, S., Garzelli, A.,
Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis,
GeoRS(40), No. 10, October 2002, pp. 2300-2312.
IEEE Top Reference. 0301
BibRef

Su, J., Wang, J., Xi, Y.,
Incremental Learning With Balanced Update on Receptive Fields for Multi-Sensor Data Fusion,
SMC-B(34), No. 1, February 2004, pp. 659-665.
IEEE Abstract. 0403

See also Nonlinear Visual Mapping Model for 3-D Visual Tracking With Uncalibrated Eye-in-Hand Robotic System. BibRef

Rao, N.S.V.[Nageswara S.V.], Reister, D.B.[David B.], Barhen, J.[Jacob],
Information Fusion Methods Based on Physical Laws,
PAMI(27), No. 1, January 2005, pp. 66-77.
IEEE Abstract. 0412
Fuse sensors so that the fused estimate is superior to all of the estimates and measurements. BibRef

Li, W., Leung, H.[Henry],
Simultaneous registration and fusion of multiple dissimilar sensors for cooperative driving,
ITS(5), No. 2, June 2004, pp. 84-98.
IEEE Abstract. 0501
BibRef

Bin, L.[Liu], Peng, J.X.[Jia-Xiong],
Image fusion method based on nonseparable wavelets,
MVA(16), No. 3, May 2005, pp. 189-196.
Springer DOI 0505
BibRef

Choi, M.,
A New Intensity-Hue-Saturation Fusion Approach to Image Fusion With a Tradeoff Parameter,
GeoRS(44), No. 6, June 2006, pp. 1672-1682.
IEEE DOI 0606
BibRef

Yao, J., Goh, K.L.,
A Refined Algorithm for Multisensor Image Registration Based on Pixel Migration,
IP(15), No. 7, July 2006, pp. 1839-1847.
IEEE DOI 0606
BibRef

Zhou, J.[Jie], Zhu, Y.M.[Yun-Min], You, Z.S.[Zhi-Sheng], Song, E.[Enbin],
An efficient algorithm for optimal linear estimation fusion in distributed multisensor systems,
SMC-A(36), No. 5, September 2006, pp. 1000-1009.
IEEE DOI 0609
BibRef

Karaçali, B.[Bilge],
Information Theoretic Deformable Registration Using Local Image Information,
IJCV(72), No. 3, May 2007, pp. 219-237.
Springer DOI 0702
Mutual information, the joint entropy, and the sum of marginal entropies of two images. BibRef

Xia, Y., Kamel, M.S.[Mohamed S.],
Novel Cooperative Neural Fusion Algorithms for Image Restoration and Image Fusion,
IP(16), No. 2, February 2007, pp. 367-381.
IEEE DOI 0702
BibRef

Wang, F.[Fei], Vemuri, B.C.[Baba C.],
Non-Rigid Multi-Modal Image Registration Using Cross-Cumulative Residual Entropy,
IJCV(74), No. 2, August 2007, pp. 201-215.
Springer DOI 0705
BibRef

Wang, F.[Fei], Vemuri, B.C.[Baba C.], Rangarajan, A.[Anand],
Groupwise point pattern registration using a novel CDF-based Jensen-Shannon Divergence,
CVPR06(I: 1283-1288).
IEEE DOI 0606
BibRef

Chen, T.[Ting], Vemuri, B.C.[Baba C.], Rangarajan, A.[Anand], Eisenschenk, S.J.[Stephan J.],
Group-Wise Point-Set Registration Using a Novel CDF-Based Havrda-Charvát Divergence,
IJCV(86), No. 1, January 2010, pp. xx-yy.
Springer DOI 1001
BibRef

Jacobson, N.P.[Nathaniel P.], Gupta, M.R.[Maya R.], Cole, J.B.,
Linear Fusion of Image Sets for Display,
GeoRS(45), No. 10, October 2007, pp. 3277-3288.
IEEE DOI 0711

See also Design Goals and Solutions for Display of Hyperspectral Images. BibRef

Jacobson, N.P.[Nathaniel P.], Gupta, M.R.[Maya R.],
SNR-Adaptive Linear Fusion of Hyperspectral Images for Color Display,
ICIP07(III: 477-480).
IEEE DOI 0709
BibRef

Orchard, J.[Jeff],
Efficient Least Squares Multimodal Registration With a Globally Exhaustive Alignment Search,
IP(16), No. 10, October 2007, pp. 2526-2534.
IEEE DOI 0711
BibRef
And:
Globally Optimal Multimodal Rigid Registration: An Analytic Solution using Edge Information,
ICIP07(I: 485-488).
IEEE DOI 0709
BibRef
Earlier:
Efficient Global Weighted Least-Squares Translation Registration in the Frequency Domain,
ICIAR05(116-124).
Springer DOI 0509
BibRef
Earlier:
Image Deformation Using Velocity Fields: An Exact Solution,
ICIAR05(439-446).
Springer DOI 0509
BibRef

Orchard, J.[Jeff], Mann, R.,
Registering a Multi-Sensor Ensemble of Images,
IP(19), No. 5, May 2010, pp. 1236-1247.
IEEE DOI 1004
BibRef

Clarkson, E.W., Kupinski, M.A., Barrett, H.H., Furenlid, L.,
A Task-Based Approach to Adaptive and Multimodality Imaging,
PIEEE(96), No. 3, March 2008, pp. 500-511.
IEEE DOI 0804
BibRef

Bentabet, L.[Layachi], Maodong, J.[Jiang],
A combined Markovian and Dirichlet sub-mixture modeling for evidence assignment: Application to image fusion,
PRL(29), No. 13, 1 October 2008, pp. 1775-1783.
Elsevier DOI 0804
Data fusion; Evidence theory; Mixture modeling; Dirichlet distribution; Markov fields; Iterated conditional modes BibRef

Jodouin, S.[Sylvie], Bentabet, L.[Layachi], Ziou, D.[Djemel], Vaillancourt, J.[Jean], Armenakis, C.[Costas],
A Combined Estimation-Deformation Model for Area Detection: Application to Topographic Area Feature Update,
PCV02(A: 181). 0305
BibRef

Zhang, Y.H.[Yin-Hui], Zhang, Y.S.[Yun-Sheng], He, Z.F.[Zi-Fen], Tang, X.Y.[Xiang-Yang],
Multiscale fusion of wavelet-domain hidden Markov tree through graph cut,
IVC(27), No. 9, 3 August 2009, pp. 1402-1410.
Elsevier DOI 0906
Wavelet-domain hidden Markov tree; Multiscale fusion; Graph cut; Tobacco leaf inspection BibRef

Zhang, Y.H.[Yin-Hui], He, Z.F.[Zi-Fen], Zhang, Y.S.[Yun-Sheng], Wu, X.[Xing],
Global optimization of wavelet-domain hidden Markov tree for image segmentation,
PR(44), No. 12, December 2011, pp. 2811-2818.
Elsevier DOI 1107
BibRef
Earlier: A1, A3, A2, Only:
Multiscale Information Fusion by Graph Cut through Convex Optimization,
ISVC10(III: 377-386).
Springer DOI 1011
Energy minimization; Multiscale; Hidden Markov tree; Global optimization; Image segmentation; Lagrange dual; Convex energy function
See also Large Displacement Dynamic Scene Segmentation through Multiscale Saliency Flow. BibRef

Kumar, M., Dass, S.,
A Total Variation-Based Algorithm for Pixel-Level Image Fusion,
IP(18), No. 9, September 2009, pp. 2137-2143.
IEEE DOI 0909
BibRef

Loza, A.[Artur], Bull, D.R.[David R.], Canagarajah, C.N.[C. Nishan], Achim, A.[Alin],
Non-Gaussian model-based fusion of noisy images in the wavelet domain,
CVIU(114), No. 1, January 2010, pp. 54-65.
Elsevier DOI 1001
Multimodal; Image fusion; Statistical modelling; Denoising BibRef

Loza, A.[Artur], Bull, D.R.[David R.], Achim, A.[Alin],
Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients,
ICIP10(3553-3556).
IEEE DOI 1009
BibRef

Anantrasirichai, N.[Nantheera], Zheng, R.[Rencheng], Selesnick, I.[Ivan], Achim, A.[Alin],
Image fusion via sparse regularization with non-convex penalties,
PRL(131), 2020, pp. 355-360.
Elsevier DOI 2004
BibRef
And: Corrigendum: PRL(136), 2020, pp. 316.
Elsevier DOI 2008
Sparse approximate solutions, Non-convex penalties, Cost function, Image fusion, Convex optimization, Multifocus image BibRef

Cvejic, N., Lewis, J., Bull, D.R., Canagarajah, C.N.,
Region-Based Multimodal Image Fusion using ICA Bases,
ICIP06(1801-1804).
IEEE DOI 0610
BibRef

Nikolov, S.G., Bull, D.R., Canagarajah, C.N., Halliwell, M., Wells, P.N.T.,
Fusion of 2-D Images Using Their Multiscale Edges,
ICPR00(Vol III: 41-44).
IEEE DOI 0009

See also Virtual Liver Biopsy: Image Processing and 3d Visualization. BibRef

Hill, P.R., Al-Mualla, M.E., Bull, D.R.,
Perceptual Image Fusion Using Wavelets,
IP(26), No. 3, March 2017, pp. 1076-1088.
IEEE DOI 1703
image fusion BibRef

Hill, P.R., Bull, D.R., Canagarajah, C.N.,
Image Fusion Using a New Framework for Complex Wavelet Transforms,
ICIP05(II: 1338-1341).
IEEE DOI 0512
BibRef
Earlier: A1, A3, A2:
Image Fusion Using Complex Wavelets,
BMVC02(Poster Session). 0208

See also Genetic Stereo Matching Using Complex Conjugate Wavelet Pyramids. BibRef

Bhatnagar, G.[Gaurav], Raman, B.[Balasubramanian],
A New Image Fusion Technique Based on Directive Contrast,
ELCVIA(8), No. 2, July 2009, pp. xx-yy.
DOI Link 1002
BibRef

Bhatnagar, G.[Gaurav], Wu, Q.M.J.[Q.M. Jonathan], Liu, Z.,
Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain,
MultMed(15), No. 5, 2013, pp. 1014-1024.
IEEE DOI 1307
Computed tomography BibRef

Bhatnagar, G.[Gaurav], Wu, Q.M.J.[Q.M. Jonathan],
A novel framework for multi-focus image fusion,
NCVPRIPG13(1-4)
IEEE DOI 1408
computer vision BibRef

Bhatnagar, G.[Gaurav], Wu, Q.M.J.[Q.M. Jonathan],
Human Visual System Based Framework for Concealed Weapon Detection,
CRV11(250-256).
IEEE DOI 1105
BibRef

Bhatnagar, G.[Gaurav], Wu, Q.M.J.[Q. M. Jonathan], Raman, B.[Balasubramanian],
Real Time Human Visual System Based Framework for Image Fusion,
ICISP10(71-78).
Springer DOI 1006

See also Novel Image Encryption Framework Based on Markov Map and Singular Value Decomposition, A. BibRef

Mitchell, H.B.,
Image Fusion: Theories, Techniques and Applications,
Springer2010, ISBN: 978-3-642-11215-7.
WWW Link. Buy this book: Image Fusion: Theories, Techniques and Applications Survey, Image Fusion. 1003
BibRef

Saeedi, J.[Jamal], Moradi, M.H.[Mohammad Hassan], Faez, K.[Karim],
A new wavelet-based fuzzy single and multi-channel image denoising,
IVC(28), No. 12, December 2010, pp. 1611-1623.
Elsevier DOI 1003
BibRef
Earlier: A1, A3, Only:
The new segmentation and fuzzy logic based multi-sensor image fusion,
IVCNZ09(328-333).
IEEE DOI 0911
Image denoising; Dual-tree discrete wavelet transform; Fuzzy membership function; Multi-channel image BibRef

Pardo-Iguzquiza, E., Rodriguez-Galiano, V.F., Chica-Olmo, M., Atkinson, P.M.[Peter M.],
Image fusion by spatially adaptive filtering using downscaling cokriging,
PandRS(66), No. 3, May 2011, pp. 337-346.
Elsevier DOI 1103
Adaptive filtering; Cokriging; Geostatistics; Image fusion; Remote sensing BibRef

Kong, W.W., Lei, Y.J., Lei, Y., Lu, S.,
Image fusion technique based on non-subsampled contourlet transform and adaptive unit-fast-linking pulse-coupled neural network,
IET-IPR(5), No. 2, April 2011, pp. 113-121.
DOI Link 1103
BibRef

Hol, J.D.,
Sensor Fusion and Calibration of Inertial Sensors, Vision, Ultra-Wideband and GPS,
Ph.D.Thesis, Linköping University, June 2011.
PDF File. 1109
BibRef

Bai, X.Z.[Xiang-Zhi], Zhou, F.[Fugen], Xue, B.D.[Bin-Dang],
Edge preserved image fusion based on multiscale toggle contrast operator,
IVC(29), No. 12, November 2011, pp. 829-839.
Elsevier DOI 1112
Toggle contrast operator; Multiscale; Image fusion; Edge preserving; Mathematical morphology BibRef

Besiris, D., Tsagaris, V., Fragoulis, N., Theoharatos, C.,
An FPGA-Based Hardware Implementation of Configurable Pixel-Level Color Image Fusion,
GeoRS(50), No. 2, February 2012, pp. 362-373.
IEEE DOI 1201
BibRef

Zeng, K., Wang, Z.,
Polyview Fusion: A Strategy to Enhance Video-Denoising Algorithms,
IP(21), No. 4, April 2012, pp. 2324-2328.
IEEE DOI 1204
BibRef

Liang, J., He, Y., Liu, D., Zeng, X.,
Image Fusion Using Higher Order Singular Value Decomposition,
IP(21), No. 5, May 2012, pp. 2898-2909.
IEEE DOI 1204
BibRef

Li, S., Yao, Z., Yi, W.,
Frame Fundamental High-Resolution Image Fusion From Inhomogeneous Measurements,
IP(21), No. 9, September 2012, pp. 4002-4015.
IEEE DOI 1208
BibRef

Zhang, Q.A.[Qi-Ang], Ma, Z.K.[Zhao-Kun], Wang, L.[Long],
Multimodality Image Fusion by Using Both Phase and Magnitude Information,
PRL(34), No. 2, 15 January 2013, pp. 185-193.
Elsevier DOI 1212
Multimodality image fusion; Shiftable complex directional pyramid transform; Phase and magnitude; Circular correlation coefficient; Weighted circular variance BibRef

Puig, L.[Luis], Sturm, P.F.[Peter F.], Guerrero, J.J.[José Jesús],
Hybrid homographies and fundamental matrices mixing uncalibrated omnidirectional and conventional cameras,
MVA(24), No. 4, May 2013, pp. 721-738.
WWW Link. 1304
BibRef
Earlier: A1, A3, A2:
Matching of omindirectional and perspective images using the hybrid fundamental matrix,
OMNIVIS08(xx-yy). 0810
BibRef

Higger, M., Akcakaya, M., Erdogmus, D.,
A Robust Fusion Algorithm for Sensor Failure,
SPLetters(20), No. 8, 2013, pp. 755-758.
IEEE DOI 1307
Bayes methods BibRef

Ciuonzo, D., Papa, G., Romano, G., Salvo Rossi, P., Willett, P.,
One-Bit Decentralized Detection With a Rao Test for Multisensor Fusion,
SPLetters(20), No. 9, 2013, pp. 861-864.
IEEE DOI 1308
sensor fusion BibRef

Ciuonzo, D., Salvo Rossi, P., Willett, P.,
Generalized Rao Test for Decentralized Detection of an Uncooperative Target,
SPLetters(24), No. 5, May 2017, pp. 678-682.
IEEE DOI 1704
Computational complexity BibRef

Montagna, R.[Roberto], Finlayson, G.D.[Graham D.],
Reducing Integrability Error of Color Tensor Gradients for Image Fusion,
IP(22), No. 10, 2013, pp. 4072-4085.
IEEE DOI 1309
BibRef
Earlier:
Reducing integrability artefacts for data fusion through colour space manipulation,
CRICV09(1955-1961).
IEEE DOI 0910
Gray-scale; image color analysis; image enhancement; image fusion. Issues with Socolinsky and Wolff fusion technique.
See also Multispectral image visualization through first-order fusion. BibRef

Connah, D.[David], Drew, M.S.[Mark Samuel], Finlayson, G.D.[Graham David],
Spectral Edge Image Fusion: Theory and Applications,
ECCV14(V: 65-80).
Springer DOI 1408
BibRef

El-Taweel, G.S., Helmy, A.K.,
Image fusion scheme based on modified dual pulse coupled neural network,
IET-IPR(7), No. 5, 2013, pp. 407-414.
DOI Link 1310
BibRef

Fu, D.J.[Dong-Jie], Chen, B.Z.[Bao-Zhang], Wang, J.[Juan], Zhu, X.L.[Xiao-Lin], Hilker, T.[Thomas],
An Improved Image Fusion Approach Based on Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model,
RS(5), No. 12, 2013, pp. 6346-6360.
DOI Link 1402
BibRef

Ionescu, B., Benois-Pineau, J., Piatrik, T., Quénot, G., (Eds.),
Fusion in Computer Vision: Understanding Complex Visual Content,

Springer2014. ISBN 978-3-319-05695-1
WWW Link. 1404
Information fusion for multimodal and multidimensional data representation. BibRef

Chaudhuri, S.[Subhasis], Kotwal, K.[Ketan],
Hyperspectral Image Fusion,

Springer2013. ISBN 978-1-4614-7469-2.
WWW Link. 1404
BibRef

Hara, K., Inoue, K., Urahama, K.,
A Differentiable Approximation Approach to Contrast-Aware Image Fusion,
SPLetters(21), No. 6, June 2014, pp. 742-745.
IEEE DOI 1404
Approximation algorithms BibRef

Jiang, Y.[Yong], Wang, M.H.[Ming-Hui],
Image fusion using multiscale edge-preserving decomposition based on weighted least squares filter,
IET-IPR(8), No. 3, March 2014, pp. 183-190.
DOI Link 1404
Award, IET IPR Premium. decomposition BibRef

Holloway, J., Mitra, K., Koppal, S.J., Veeraraghavan, A.N.,
Generalized Assorted Camera Arrays: Robust Cross-Channel Registration and Applications,
IP(24), No. 3, March 2015, pp. 823-835.
IEEE DOI 1502
cameras BibRef

Woo, J.Y.[Jongh-Ye], Stone, M., Prince, J.L.,
Multimodal Registration via Mutual Information Incorporating Geometric and Spatial Context,
IP(24), No. 2, February 2015, pp. 757-769.
IEEE DOI 1502
edge detection BibRef

Bu, S.H.[Shu-Hui], Cheng, S.G.[Shao-Guang], Liu, Z.B.[Zhen-Bao], Han, J.W.[Jun-Wei],
Multimodal Feature Fusion for 3D Shape Recognition and Retrieval,
MultMedMag(21), No. 4, October 2014, pp. 38-46.
IEEE DOI 1502
Boltzmann machines BibRef

Liu, Y.[Yu], Wang, Z.F.[Zeng-Fu],
Simultaneous image fusion and denoising with adaptive sparse representation,
IET-IPR(9), No. 5, 2015, pp. 347-357.
DOI Link 1506
image classification Award, IET IPR Premium. BibRef

Liu, Y.[Yu], Chen, X.[Xun], Ward, R.K.[Rabab K.], Wang, Z.J.[Z. Jane],
Image Fusion With Convolutional Sparse Representation,
SPLetters(23), No. 12, December 2016, pp. 1882-1886.
IEEE DOI 1612
image coding BibRef

Saska, D., Blum, R.S., Kaplan, L.,
Fusion of Quantized and Unquantized Sensor Data for Estimation,
SPLetters(22), No. 11, November 2015, pp. 1927-1930.
IEEE DOI 1509
Gaussian noise BibRef

Latorre-Carmona, P., Pla, F., Stern, A., Moon, I., Javidi, B.,
Three-Dimensional Imaging With Multiple Degrees of Freedom Using Data Fusion,
PIEEE(103), No. 9, September 2015, pp. 1654-1671.
IEEE DOI 1509
E.g. 3-D imaging integrated with polarimetric and multispectral imaging. Arrays BibRef

Chen, C.[Chen], Li, Y.Q.[Ye-Qing], Liu, W.[Wei], Huang, J.Z.[Jun-Zhou],
SIRF: Simultaneous Satellite Image Registration and Fusion in a Unified Framework,
IP(24), No. 11, November 2015, pp. 4213-4224.
IEEE DOI 1509
BibRef
Earlier:
Image Fusion with Local Spectral Consistency and Dynamic Gradient Sparsity,
CVPR14(2760-2765)
IEEE DOI 1409
geophysical image processing BibRef

Connah, D.[David], Drew, M.S.[Mark S.], Finlayson, G.D.[Graham D.],
Spectral edge: gradient-preserving spectral mapping for image fusion,
JOSA-A(32), No. 12, December 2015, pp. 2384-2396.
DOI Link 1601
Image processing BibRef

Son, C.H., Zhang, X.P.,
Layer-Based Approach for Image Pair Fusion,
IP(25), No. 6, June 2016, pp. 2866-2881.
IEEE DOI 1605
Color BibRef

Albiol, F., Corbi, A., Albiol, A.,
Geometrical Calibration of X-Ray Imaging With RGB Cameras for 3D Reconstruction,
MedImg(35), No. 8, August 2016, pp. 1952-1961.
IEEE DOI 1608
Biomedical imaging BibRef

Ao, B.[Buke], Wang, Y.[Yongcai], Yu, L.[Lu], Brooks, R.R.[Richard R.], Iyengar, S.S.,
On Precision Bound of Distributed Fault-Tolerant Sensor Fusion Algorithms,
Surveys(49), No. 1, July 2016, pp. Article No 5.
DOI Link 1608
Sensors have limited precision and accuracy. They extract data from the physical environment, which contains noise. The goal of sensor fusion is to make the final decision robust, minimizing the influence of noise and system errors. One problem that has not been adequately addressed is establishing the bounds of fusion result precision. Precision is the maximum range of disagreement that can be introduced by one or more faulty inputs. This definition of precision is consistent both with Lamport's Byzantine Generals problem and the mini-max criteria commonly found in game theory. This article considers the precision bounds of several fault-tolerant information fusion approaches, including Byzantine agreement, Marzullo's interval-based approach, and the Brooks-Iyengar fusion algorithm. We derive precision bounds for these fusion algorithms. The analysis provides insight into the limits imposed by fault tolerance and guidance for applying fusion approaches to applications. BibRef

Liu, X.B.[Xing-Bin], Mei, W.B.[Wen-Bo], Du, H.Q.[Hui-Qian], Bei, J.[Jiadi],
A novel image fusion algorithm based on nonsubsampled shearlet transform and morphological component analysis,
SIViP(10), No. 5, May 2016, pp. 959-966.
Springer DOI 1608
BibRef

Adu, J.H.[Jian-Hua], Xie, S.H.[Sheng-Hua], Gan, J.H.[Jian-Hong],
Image fusion based on visual salient features and the cross-contrast,
JVCIR(40, Part A), No. 1, 2016, pp. 218-224.
Elsevier DOI 1609
Image fusion BibRef

Liao, B.[Bin], Yan, L.[Lei], Mo, W.[Wei], Shen, J.[Jing], Zhang, W.Y.[Wen-Yao],
Coherence restricted StOMP and its application in image fusion,
JVCIR(40, Part B), No. 1, 2016, pp. 559-573.
Elsevier DOI 1610
Sparse representation BibRef

Mustaniemi, J.[Janne], Kannala, J.H.[Ju-Ho], Heikkilä, J.[Janne],
Parallax correction via disparity estimation in a multi-aperture camera,
MVA(27), No. 8, November 2016, pp. 1313-1323.
Springer DOI 1612
BibRef
Earlier:
Disparity Estimation for Image Fusion in a Multi-aperture Camera,
CAIP15(II:158-170).
Springer DOI 1511
BibRef

Ancuti, C.O.[Codruta Orniana], Ancuti, C.[Cosmin], de Vleeschouwer, C.[Christophe], Bovik, A.C.,
Single-Scale Fusion: An Effective Approach to Merging Images,
IP(26), No. 1, January 2017, pp. 65-78.
IEEE DOI 1612
image fusion
See also Color Balance and Fusion for Underwater Image Enhancement. BibRef

Gesto-Diaz, M., Tombari, F., Gonzalez-Aguilera, D., Lopez-Fernandez, L., Rodriguez-Gonzalvez, P.,
Feature matching evaluation for multimodal correspondence,
PandRS(129), No. 1, 2017, pp. 179-188.
Elsevier DOI 1706
Features. 28 different combinations of detectors. BibRef

Wang, S.P.[Shi-Ping], Guo, W.Z.[Wen-Zhong],
Sparse Multigraph Embedding for Multimodal Feature Representation,
MultMed(19), No. 7, July 2017, pp. 1454-1466.
IEEE DOI 1706
Clustering algorithms, Correlation, Data integration, Feature extraction, Learning systems, Optimization, Sparse matrices, Feature fusion, graph embedding, machine learning, multimodal data, sparse, representation BibRef

Fakhari, F.[Fatemeh], Mosavi, M.R.[Mohammad R.], Lajvardi, M.M.[Mehdi M.],
Image fusion based on multi-scale transform and sparse representation: an image energy approach,
IET-IPR(11), No. 11, November 2017, pp. 1041-1049.
DOI Link 1711
BibRef

Liu, M.[Min], Wang, X.P.[Xue-Ping], Zhang, H.Z.[Hong-Zhong],
Classification of nematode image stacks by an information fusion based multilinear approach,
PRL(100), No. 1, 2017, pp. 22-28.
Elsevier DOI 1712
BibRef
And:
A multi-direction image fusion based approach for classification of multi-focal nematode image stacks,
ICIP17(3835-3839)
IEEE DOI 1803
Multi-focal images within a stack are fused along 3 orthogonal directions. Discrete wavelet transforms, Encoding, Hafnium, VLIW, Canonical correlation analysis, Image classification, Multi-focal images BibRef

Liu, M.[Min], Wang, X.P.[Xue-Ping], Liu, X.Y.[Xiao-Yan], Zhang, H.Z.[Hong-Zhong],
Classification of multi-focal nematode image stacks using a projection based multilinear approach,
ICIP17(595-599)
IEEE DOI 1803
Indexes, Tensile stress, VLIW, Digital Multi-focal Images, Image classification, Multilinear analysis, Projection BibRef

Liu, M.[Min], Roy-Chowdhury, A.K.[Amit K.], Yoder, M.[Melissa], de Ley, P.[Paul],
Multi-focal nematode image classification using the 3D X-Ray Transform,
ICIP10(269-272).
IEEE DOI 1009
BibRef

Liu, M.[Min], Wang, X.P.[Xue-Ping], Liu, K.[Keran], Liu, X.Y.[Xiao-Yan],
Multi-focal nematode image stack classification using a projection-based multi-linear method,
MVA(29), No. 1, January 2018, pp. 135-144.
WWW Link. 1801
BibRef

Xiang, F.T.[Feng-Tao], Jian, Z.[Zhang], Liang, P.[Pan], Xue-Qiang, G.[Gu],
Robust image fusion with block sparse representation and online dictionary learning,
IET-IPR(12), No. 3, March 2018, pp. 345-353.
DOI Link 1802
BibRef

Nandal, A.[Amita], Bhaskar, V.[Vidhyacharan],
Fuzzy enhanced image fusion using pixel intensity control,
IET-IPR(12), No. 3, March 2018, pp. 453-464.
DOI Link 1802
BibRef

Mohammad, F.R., Ciuonzo, D., Mohammed, Z.A.K.,
Mean-Based Blind Hard Decision Fusion Rules,
SPLetters(25), No. 5, May 2018, pp. 630-634.
IEEE DOI 1805
probability, sensor fusion, signal detection, wireless channels, Neyman-Pearson criterion, blind alternatives, nonrandomized tests BibRef

Ding, S.F.[Shi-Fei], Zhao, X.Y.[Xing-Yu], Xu, H.[Hui], Zhu, Q.B.[Qiang-Bo], Xue, Y.[Yu],
NSCT-PCNN image fusion based on image gradient motivation,
IET-CV(12), No. 4, June 2018, pp. 377-383.
DOI Link 1805
BibRef

Wang, Q.[Qinxia], Yang, X.P.[Xiao-Ping],
Variational image fusion approach based on TGV and local information,
IET-CV(12), No. 4, June 2018, pp. 535-541.
DOI Link 1805
BibRef

Cun, X.D.[Xiao-Dong], Pun, C.M.[Chi-Man], Gao, H.[Hao],
Applying stochastic second-order entropy images to multi-modal image registration,
SP:IC(65), 2018, pp. 201-209.
Elsevier DOI 1805
Multi-modal image registration, Image-processing, Image matching, Entropy image, Structural representation, Second order entropy BibRef

Xie, D.H.[Dong-Hui], Gao, F.[Feng], Sun, L.[Liang], Anderson, M.[Martha],
Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Li, D.C.[Da-Cheng], Li, Y.R.[Yan-Rong], Yang, W.[Wenfu], Ge, Y.Q.[Yan-Qin], Han, Q.J.[Qi-Jin], Ma, L.L.[Ling-Ling], Chen, Y.H.[Yong-Hong], Li, X.[Xuan],
An Enhanced Single-Pair Learning-Based Reflectance Fusion Algorithm with Spatiotemporally Extended Training Samples,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

He, G.Q.[Gui-Qing], Xing, S.Y.[Si-Yuan], He, X.J.[Xing-Jian], Wang, J.[Jun], Fan, J.P.[Jian-Ping],
Image fusion method based on simultaneous sparse representation with non-subsampled contourlet transform,
IET-CV(13), No. 2, March 2019, pp. 240-248.
DOI Link 1902
BibRef

El-Hoseny, H.M.[Heba M.], El-Rahman, W.A.[Wael Abd], El-Shafai, W.[Walid], El-Rabaie, E.S.M.[El-Sayed M.], Mahmoud, K.R.[Korany R.], El-Samie, F.E.A.[Fathi E. Abd], Faragallah, O.S.[Osama S.],
Optimal multi-scale geometric fusion based on non-subsampled contourlet transform and modified central force optimization,
IJIST(29), No. 1, March 2019, pp. 4-18.
WWW Link. 1902
BibRef

Wang, M.[Meng], Liu, X.W.[Xing-Wang], Jin, H.P.[Huai-Ping],
A Generative Image Fusion Approach Based on Supervised Deep Convolution Network Driven by Weighted Gradient Flow,
IVC(86), 2019, pp. 1-16.
Elsevier DOI 1906
Deep convolution neural network, Deep generative model, Image fusion, Dual-CNN, Differential gradient flow BibRef

Hu, Y.X.[Yan-Xiang], Gao, Q.[Qian], Zhang, B.[Bo], Zhang, J.T.[Jun-Tong],
On the use of joint sparse representation for image fusion quality evaluation and analysis,
JVCIR(61), 2019, pp. 225-235.
Elsevier DOI 1906
Image fusion, Quality evaluation, Sparse representation, Joint sparse representation, Atom remnant analysis BibRef

Yao, W.[Wei], Jiang, Y.[Ying], Lu, W.[Wenda], Chen, J.[Jun], Xie, L.C.[Lin-Chao],
RETRACTED: Deeply fusing multimodal features in hypergraph,
JVCIR(69), 2020, pp. 102836.
Elsevier DOI 2006
BibRef
And: Original: JVCIR(62), 2019, pp. 97-104. 1908
Multimodel, Deeply fusing BibRef

Li, J.[Jie], Liu, X.X.[Xin-Xin], Yuan, Q.Q.[Qiang-Qiang], Shen, H.F.[Huan-Feng], Zhang, L.P.[Liang-Pei],
Antinoise Hyperspectral Image Fusion by Mining Tensor Low-Multilinear-Rank and Variational Properties,
GeoRS(57), No. 10, October 2019, pp. 7832-7848.
IEEE DOI 1910
Gaussian noise, geophysical image processing, hyperspectral imaging, image fusion, image resolution, variational optimization BibRef

Xing, C.D.[Chang-Da], Wang, Z.S.[Zhi-Sheng], Ouyang, Q.[Quan], Dong, C.[Chong], Duan, C.W.[Chao-Wei],
Image fusion method based on spatially masked convolutional sparse representation,
IVC(90), 2019, pp. 103806.
Elsevier DOI 1912
Sparse representation (SR), Image fusion, Spatially masked convolutional sparse representation (SMCSR), Two-scale gradient optimization BibRef

Tan, Z.Y.[Zhen-Yu], Di, L.P.[Li-Ping], Zhang, M.D.[Ming-Da], Guo, L.Y.[Li-Ying], Gao, M.L.[Mei-Ling],
An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Li, C.X.[Cheng-Xi], He, Y.[You], Wang, X.Q.[Xue-Qian], Li, G.[Gang], Varshney, P.K.[Pramod K.],
Distributed Detection of Sparse Stochastic Signals via Fusion of 1-bit Local Likelihood Ratios,
SPLetters(26), No. 12, December 2019, pp. 1738-1742.
IEEE DOI 2001
Fusion of 1 bit detections. quantisation (signal), sensor fusion, signal detection, stochastic processes, wireless sensor networks, 1-bit quantization BibRef

Farzaneh, A.H.[Amir Hossein], Qi, X.J.[Xiao-Jun],
Cross-spectral registration of natural images with SIPCFE,
MVA(31), No. 1, January 2020, pp. Article 10.
WWW Link. 2003
BibRef

Cao, S., Shen, H., Chen, S., Li, C.,
Boosting Structure Consistency for Multispectral and Multimodal Image Registration,
IP(29), 2020, pp. 5147-5162.
IEEE DOI 2004
Transforms, Image registration, Boosting, Correlation, Entropy, Image edge detection, Histograms, Multispectral image, optimization BibRef

Wang, Q.L.[Qiao-Lu], Gao, Z.S.[Zhi-Sheng], Xie, C.Z.[Chun-Zhi], Chen, G.P.[Gong-Ping], Luo, Q.Q.[Qing-Qing],
Fractional-order total variation for improving image fusion based on saliency map,
SIViP(14), No. 5, July 2020, pp. 991-999.
Springer DOI 2006
BibRef

Zhou, H.[Hui], Peng, J.H.[Jian-Hua], Liao, C.[Changwu], Li, J.[Jue],
Application of deep learning model based on image definition in real-time digital image fusion,
RealTimeIP(17), No. 3, June 2020, pp. 643-654.
Springer DOI 2006
BibRef

Pamart, A.[Anthony], Morlet, F.[François], de Luca, L.[Livio], Veron, P.[Philippe],
A Robust and Versatile Pipeline for Automatic Photogrammetric-Based Registration of Multimodal Cultural Heritage Documentation,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Meher, B.[Bikash], Agrawal, S.[Sanjay], Panda, R.[Rutuparna], Dora, L.[Lingraj], Abraham, A.[Ajith],
A novel region-based multimodal image fusion technique using improved dictionary learning,
IJIST(30), No. 3, 2020, pp. 558-576.
DOI Link 2008
dictionary learning, image fusion, region-based fusion, sparse representation BibRef

Shen, G.R.[Guo-Rong],
Image understanding via learning weakly-supervised cross-modal semantic translation,
JVCIR(71), 2020, pp. 102789.
Elsevier DOI 2009
Image understanding, Cross-modal semantic translation, Weakly-supervised learning BibRef

Wang, S.Y.[Shi-Ying], Shen, Y.[Yan],
Multi-modal image fusion based on saliency guided in NSCT domain,
IET-IPR(14), No. 13, November 2020, pp. 3188-3201.
DOI Link 2012
NSCT: Non-Subsampled Contourlet Transform. BibRef

Shen, D.H.[Dong-Hao], Zareapoor, M.[Masoumeh], Yang, J.[Jie],
Multimodal image fusion based on point-wise mutual information,
IVC(105), 2021, pp. 104047.
Elsevier DOI 2101
Image fusion, Multimodal, Point-wise mutual information, Markov random field model, Gradient domain BibRef

Kavipriya, A., Muthukumar, A.,
Special Issue Retraction: Innovative approach for multimodal fusion recognition based feature extraction using band-limited phase-only correlation and discrete orthonormal Stockwell transform,
IET-IPR(17), No. 1, January 2023, pp. 301.
DOI Link 2301
BibRef
And: IET-IPR(14), No. 15, 15 December 2020, pp. 3669-3675.
DOI Link 2103
BibRef

Lee, H.[Hyungtae], Kwon, H.S.[Hee-Sung],
DBF: Dynamic Belief Fusion for Combining Multiple Object Detectors,
PAMI(43), No. 5, May 2021, pp. 1499-1514.
IEEE DOI 2104
Detectors, Object detection, Bayes methods, Feature extraction, Probabilistic logic, Convolutional neural networks, dempster-shafer theory BibRef

Cao, Y., Lee, H., Kwon, H.,
Enhanced object detection via fusion with prior beliefs from image classification,
ICIP17(920-924)
IEEE DOI 1803
Clustering algorithms, Degradation, Detectors, Heuristic algorithms, Image classification, Object detection, object detection BibRef

Fang, A.Q.[Ai-Qing], Zhao, X.B.[Xin-Bo], Yang, J.Q.[Jia-Qi], Zhang, Y.N.[Yan-Ning], Zheng, X.[Xiang],
Non-linear and selective fusion of cross-modal images,
PR(119), 2021, pp. 108042.
Elsevier DOI 2106
Image fusion, Deep learning, Non-linear characteristic, Feature selection characteristic BibRef

Zhao, F.[Fan], Zhao, W.[Wenda],
Learning Specific and General Realm Feature Representations for Image Fusion,
MultMed(23), 2021, pp. 2745-2756.
IEEE DOI 2109
Image fusion, Feature extraction, Biomedical imaging, Image edge detection, Visualization, Remote sensing, Transforms, no-reference perceptual metric loss BibRef

Jing, L.L.[Long-Long], Tian, Y.L.[Ying-Li],
Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey,
PAMI(43), No. 11, November 2021, pp. 4037-4058.
IEEE DOI 2110
Task analysis, Visualization, Videos, Training, Learning systems, Feature extraction, Annotations, Self-supervised learning, deep learning BibRef

Jing, L.L.[Long-Long], Zhang, L.[Ling], Tian, Y.L.[Ying-Li],
Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences,
MULA21(1581-1891)
IEEE DOI 2109
Image segmentation, Image recognition, Shape, Supervised learning, Feature extraction, Graph neural networks BibRef

Liu, B.[Bin],
Robust Dynamic Multi-Modal Data Fusion: A Model Uncertainty Perspective,
SPLetters(28), 2021, pp. 2107-2111.
IEEE DOI 2112
Data models, Heuristic algorithms, Signal processing algorithms, Uncertainty, Computational modeling, Bayes methods, Task analysis, particle filter BibRef

Yang, Z.J.[Zi-Jun], Diao, C.Y.[Chun-Yuan], Li, B.[Bo],
A Robust Hybrid Deep Learning Model for Spatiotemporal Image Fusion,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Liu, J.Y.[Jin-Yuan], Fan, X.[Xin], Jiang, J.[Ji], Liu, R.S.[Ri-Sheng], Luo, Z.X.[Zhong-Xuan],
Learning a Deep Multi-Scale Feature Ensemble and an Edge-Attention Guidance for Image Fusion,
CirSysVideo(32), No. 1, January 2022, pp. 105-119.
IEEE DOI 2201
Feature extraction, Image edge detection, Image fusion, Training, Deep learning, Task analysis, Dictionaries, Image fusion, attention mechanism BibRef

Lei, D.J.[Da-Jiang], Ran, G.S.[Gang-Sheng], Zhang, L.P.[Li-Ping], Li, W.S.[Wei-Sheng],
A Spatiotemporal Fusion Method Based on Multiscale Feature Extraction and Spatial Channel Attention Mechanism,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Zhao, S.M.[Shang-Min], Liu, J.[Jiao], Cheng, W.M.[Wei-Ming], Zhou, C.H.[Cheng-Hu],
Fusion Scheme and Implementation Based on SRTM1, ASTER GDEM V3, and AW3D30,
IJGI(11), No. 3, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Öfverstedt, J.[Johan], Lindblad, J.[Joakim], Sladoje, N.[Nataša],
Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment,
PRL(159), 2022, pp. 196-203.
Elsevier DOI 2206
Mutual information, Image alignment, Global optimization, Multimodal, Entropy BibRef

Maggiolo, L.[Luca], Solarna, D.[David], Moser, G.[Gabriele], Serpico, S.B.[Sebastiano Bruno],
Registration of Multisensor Images through a Conditional Generative Adversarial Network and a Correlation-Type Similarity Measure,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Zhang, X.[Xin], Jiang, H.Z.[Hang-Zhi], Xu, N.[Nuo], Ni, L.[Lei], Huo, C.L.[Chun-Lei], Pan, C.H.[Chun-Hong],
MsIFT: Multi-Source Image Fusion Transformer,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Marivani, I.[Iman], Tsiligianni, E.[Evaggelia], Cornelis, B.[Bruno], Deligiannis, N.[Nikos],
Designing CNNs for Multimodal Image Restoration and Fusion via Unfolding the Method of Multipliers,
CirSysVideo(32), No. 9, September 2022, pp. 5830-5845.
IEEE DOI 2209
Image restoration, Image fusion, Computational modeling, Convolutional neural networks, Task analysis, Image resolution, multimodal CNN BibRef

Luo, X.Q.[Xiao-Qing], Gao, Y.H.[Yuan-Hao], Wang, A.[Anqi], Zhang, Z.[Zhancheng], Wu, X.J.[Xiao-Jun],
IFSepR: A General Framework for Image Fusion Based on Separate Representation Learning,
MultMed(25), 2023, pp. 608-623.
IEEE DOI 2302
Image fusion, Feature extraction, Task analysis, Image reconstruction, Decoding, Transforms, Knowledge engineering BibRef

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Convolutional codes, Image segmentation, Visualization, Fuses, Linguistics, Benchmark testing, Referring Image Segmentation, Context Modeling BibRef

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Training, Integrated optics, Deep learning, Image coding, Fuses, Simulation, Optical computing, End-to-End Optimization, Deep learning BibRef

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ICIP21(1874-1878)
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CVPR06(II: 1777-1783).
IEEE DOI 0606
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Chen, Z.Y.[Zhi-Yu], Zheng, Y.[Yue], Abidi, B.R., Page, D.L., Abidi, M.A.,
A Combinational Approach to the Fusion, De-noising and Enhancement of Dual-Energy X-Ray Luggage Images,
OTCBVS05(III: 2-2).
IEEE DOI 0507
Fusion of X-Ray images to improve recognition. BibRef

Abidi, B.R., Zheng, Y., Gribok, A.V., Abidi, M.A.,
Screener Evaluation of Pseudo-Colored Single Energy X-ray Luggage Images,
EEMCV05(III: 35-35).
IEEE DOI 0507
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Talbi, H.[Hichem], Batouche, M.[Mohamed], Draa, A.[Amer],
A Quantum-Inspired Genetic Algorithm for Multi-source Affine Image Registration,
ICIAR04(I: 147-154).
Springer DOI 0409
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Wu, Y.[Yan], Li, M.[Ming], Liao, G.S.[Gui-Sheng],
Image fusion by means of A trous discrete wavelet decomposition,
ICARCV04(II: 1538-1542).
IEEE DOI 0412
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Lee, S.C.[Sang-Chul], Bajesly, P.,
Multisensor raster and vector data fusion based on uncertainty modeling,
ICIP04(V: 3355-3358).
IEEE DOI 0505
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Chen, T.[Tao], Guo, R.[Ruosan], Peng, S.L.[Si-Long],
Image fusion using weighted multiscale fundamental form,
ICIP04(V: 3319-3322).
IEEE DOI 0505
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Zhang, J.Y.[Jun-Ying], Wei, L.[Le], Miao, Q.G.[Qi-Guang], Wang, Y.[Yue],
Image fusion based on non-negative matrix factorization,
ICIP04(II: 973-976).
IEEE DOI 0505
BibRef

Zöllei, L.[Lilla], Wells, III, W.M.[William M.],
Multi-modal Image Registration Using Dirichlet-Encoded Prior Information,
WBIR06(34-42).
Springer DOI 0607
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Zollei, L.[Lilla], Fisher, J.[John], Wells, III, W.M.[William M.],
A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration,
MIT AIM-2004-011, April 28, 2004.
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Rabel, M., Schmeiser, A., Grossmann, H.P.,
Communication architecture for sensorfusion systems,
IVS04(363-368).
IEEE DOI 0411
BibRef

Ikeda, T., Ishiguro, H., Asada, M.,
Sensor fusion as optimization: maximizing mutual information between sensory signals,
ICPR04(II: 501-504).
IEEE DOI 0409
BibRef

Escalante-Ramirez, B., Lopez-Caloca, A.,
Image fusion with the hermite transform,
ICIP03(II: 145-148).
IEEE DOI 0312
BibRef

Lam, E.Y.,
Graylevel alignment between two images using linear programming,
ICIP03(II: 327-330).
IEEE DOI 0312
BibRef

Eom, K.B.,
Fusion of multiple images with robust random field models,
ICIP03(II: 335-338).
IEEE DOI 0312
BibRef

Chan, H.M.[Ho-Ming], Chung, A.C.S., Yu, S.C.H., Norbash, A., Wells, W.M.,
Multi-modal image registration by minimizing Kullback-Leibler distance between expected and observed joint class histograms,
CVPR03(II: 570-576).
IEEE DOI 0307
BibRef

Schlesinger, D.[Dmitrij], Flach, B.[Boris],
A Probabilistic Segmentation Scheme,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Flach, B., Kask, E., Schlesinger, D., Skulish, A.,
Unifying Registration and Segmentation for Multi-sensor Images,
DAGM02(190 ff.).
Springer DOI 0303
BibRef

Moxey, C.E., Sangwine, S.J., Ell, T.A.,
Color-grayscale image registration using hypercomplex phase correlation,
ICIP02(II: 385-388).
IEEE DOI 0210

See also Colour image filters based on hypercomplex convolution. BibRef

Lavest, J.M., Guichard, F., Rousseau, C.,
Multi-view reconstruction combining underwater and air sensors,
ICIP02(III: 813-816).
IEEE DOI 0210
BibRef

Yang, J.Z.[Jin-Zhong], Blum, R.S.[Rick S.],
A statistical signal processing approach to image fusion for conceled weapon detection,
ICIP02(I: 513-516).
IEEE DOI 0210
BibRef

Ghassemian, H.,
Multi-sensor Image Fusion Using Multirate Filter Banks,
ICIP01(I: 846-849).
IEEE DOI 0108
BibRef

Ma, B., Lakshmanan, S., Hero, A.O.,
A Robust Bayesian Multisensor Fusion Algorithm for Joint Lane and Pavement Boundary Detection,
ICIP01(I: 762-765).
IEEE DOI 0108
BibRef

Flandin, G.[Grégory], Chaumette, F.[François],
Visual Data Fusion for Objects Localization by Active Vision,
ECCV02(IV: 312 ff.).
Springer DOI 0205
BibRef
Earlier:
Visual Data Fusion: Application to Objects Localization and Exploration,
INRIARR-4168, April 2001.
HTML Version.
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BibRef

Scheunders, P.,
Multispectral Image Fusion Using Local Mapping Techniques,
ICPR00(Vol II: 311-314).
IEEE DOI 0009
BibRef

Bloch, I., Aurdal, L., Bijno, D., and Muller, J.,
Estimation of Class Membership Functions for Grey-Level Based Image Fusion,
ICIP97(III: 268-271).
IEEE DOI BibRef 9700

Shah, S., Aggarwal, J.K., Eledath, J., and Ghosh, J.,
Multisensor Integration for Scene Classification: an Experiment in Human Form Detection,
ICIP97(II: 199-202).
IEEE DOI BibRef 9700

Voyles, R.M.[Richard M.], Morrow, J.D.[J. Dan], and Khosla, P.K.[Pradeep K.],
Including Sensor Bias in Shape from Motion Calibration and Multisensor Fusion,
MSFIIS96(xx). BibRef 9600

Hilton, A., Illingworth, J.,
Multi Resolution Geometric Fusion,
3DIM97(8 - Object Modeling) 9702
BibRef

Laferte, J.M., Heitz, F., Perez, P., Fabre, E.,
Hierarchical Statistical Models for the Fusion of Multiresolution Image Data,
ICCV95(908-913).
IEEE DOI BibRef 9500

Hode, Y., Deruyver, A., Bendriem, B., Volkow, N.,
Temporal image fusion,
ICIP95(II: 472-475).
IEEE DOI 9510
BibRef

Koren, I., Laine, A., Taylor, F.,
Image fusion using steerable dyadic wavelet transform,
ICIP95(III: 232-235).
IEEE DOI 9510
BibRef

Chipman, L.J., Orr, T.M., Graham, L.N.,
Wavelets and image fusion,
ICIP95(III: 248-251).
IEEE DOI 9510
BibRef

Aloimonos, Y.F.[Yi-Fannis], Fermüller, C.[Cornelia], and Stuart, B.[Bradley],
Medusa Synthesized,
ARPA94(I:645-659). BibRef 9400

Wang, Y.F.[Yuan-Fang],
New Method for Sensor Data Fusion in Machine Vision,
SPIE(1570), 1991, pp. 31-42. BibRef 9100

Swan, J.[John], Shields, F.J.[Frank J.],
Multisensor Fusion Methodologies Compared,
SPIE(1483), 1991, pp. 219-230. BibRef 9100

Bernander, Ö.[Öjvind], Koch, C.[Christof],
Local cross-modality image alignment using unsupervised learning,
ECCV90(573-575).
Springer DOI 9004
BibRef

Duncan, J.S., Gindi, G.R., Narendra, K.S.,
Low Level Information Fusion: Multisensor Scene Segmentation Using Learning Automata,
SRMSF87(323-333). BibRef 8700

Huntsberger, T.L., Jayaramamurthy, S.N.,
A Framework for Multi-Sensor Fusion in the Presence of Uncertainty,
SRMSF87(345-350). BibRef 8700

Chen, S.S.,
A Geometric Approach To Multisensor Fusion And Spatial Reasoning,
SRMSF87(201-210). BibRef 8700

Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Image and Sensor Fusion -- Review and Survey Articles, Evaluations .


Last update:Mar 16, 2024 at 20:36:19