19.4.3.19.3 Hyperspectral, Spectral Reconstruction from RGB

Chapter Contents (Back)
High Dynamic Range. Hyperspectral Reconstruction. RGB to Hyperspectral.

Laamanen, H.[Hannu], Jetsu, T.[Tuija], Jaaskelainen, T.[Timo], Parkkinen, J.[Jussi],
Weighted compression of spectral color information,
JOSA-A(25), No. 6, June 2008, pp. 1383-1388.
DOI Link 0711
BibRef

Li, H.Y.[Hong-Yu], Wu, Z.J.[Zhu-Jing], Zhang, L.[Lin], Parkkinen, J.[Jussi],
SR-LLA: A novel spectral reconstruction method based on locally linear approximation,
ICIP13(2029-2033)
IEEE DOI 1402
Munsell dataset, Spectral reconstruction, locally linear approximation BibRef

Murakami, Y.[Yuri], Yamaguchi, M.[Masahiro], Ohyama, N.[Nagaaki],
Class-based spectral reconstruction based on unmixing of low-resolution spectral information,
JOSA-A(28), No. 7, July 2011, pp. 1470-1481.
WWW Link. 1107
BibRef

Li, Y.Q.[Yu-Qi], Wang, C.[Chong], Zhao, J.Y.[Jie-Yu],
Locally Linear Embedded Sparse Coding for Spectral Reconstruction From RGB Images,
SPLetters(25), No. 3, March 2018, pp. 363-367.
IEEE DOI 1802
Cameras, Dictionaries, Feature extraction, Image color analysis, Image reconstruction, Image resolution, Training, spectral reconstruction BibRef

Nguyen, R.M.H.[Rang M. H.], Brown, M.S.[Michael S.],
RAW Image Reconstruction Using a Self-contained sRGB-JPEG Image with Small Memory Overhead,
IJCV(126), No. 6, June 2018, pp. 637-650.
Springer DOI 1804
BibRef
Earlier:
RAW Image Reconstruction Using a Self-Contained sRGB-JPEG Image with Only 64 KB Overhead,
CVPR16(1655-1663)
IEEE DOI 1612
BibRef

Nguyen, R.M.H.[Rang M. H.], Prasad, D.K.[Dilip K.], Brown, M.S.[Michael S.],
Training-Based Spectral Reconstruction from a Single RGB Image,
ECCV14(VII: 186-201).
Springer DOI 1408
BibRef
Earlier: A2, A1, A3:
Quick Approximation of Camera's Spectral Response from Casual Lighting,
CVPV13(844-851)
IEEE DOI 1403
approximation theory BibRef

Li, Y.Q.[Yu-Qi], Wang, C.[Chong], Zhao, J.Y.[Jie-Yu], Yuan, Q.S.[Qing-Shu],
Efficient spectral reconstruction using a trichromatic camera via sample optimization,
VC(34), No. 12, December 2018, pp. 1773-1783.
Springer DOI 1811
BibRef

Han, X., Yu, J., Luo, J., Sun, W.,
Reconstruction From Multispectral to Hyperspectral Image Using Spectral Library-Based Dictionary Learning,
GeoRS(57), No. 3, March 2019, pp. 1325-1335.
IEEE DOI 1903
geophysical image processing, hyperspectral imaging, image classification, image fusion, image matching, spectral library BibRef

Rout, L.,
ALERT: Adversarial Learning With Expert Regularization Using Tikhonov Operator for Missing Band Reconstruction,
GeoRS(58), No. 6, June 2020, pp. 4395-4405.
IEEE DOI 2005
Adversarial learning, expert regularization, missing band reconstruction, remote sensing, Tikhonov operator BibRef

Wang, B.L.[Ben-Lin], An, R.[Ru], Jiang, T.[Tong], Xing, F.[Fei], Ju, F.[Feng],
Image Spectral Resolution Enhancement for Mapping Native Plant Species in a Typical Area of the Three-River Headwaters Region, China,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Li, J.J.[Jiao-Jiao], Wu, C.X.[Chao-Xiong], Song, R.[Rui], Li, Y.S.[Yun-Song], Xie, W.Y.[Wei-Ying],
Residual Augmented Attentional U-Shaped Network for Spectral Reconstruction from RGB Images,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Dai, S.F.[Shao-Fei], Liu, W.[Wenbo], Wang, Z.Y.[Zheng-Yi], Li, K.Y.[Kai-Yu],
A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Paul, S., Nagesh Kumar, D.,
Transformation of Multispectral Data to Quasi-Hyperspectral Data Using Convolutional Neural Network Regression,
GeoRS(59), No. 4, April 2021, pp. 3352-3368.
IEEE DOI 2104
Earth, Data models, Remote sensing, Artificial satellites, Spatial resolution, Agriculture, quasi-HS data BibRef

Park, J.E.[Jeong-Eun], Kim, G.[Goo], Hong, S.[Sungwook],
Green Band Generation for Advanced Baseline Imager Sensor Using Pix2Pix With Advanced Baseline Imager and Advanced Himawari Imager Observations,
GeoRS(59), No. 8, August 2021, pp. 6415-6423.
IEEE DOI 2108
Role in monitoring water and vegetation information. No green band in GOES-16. Green products, Satellites, Air pollution, Data models, Training, Vegetation mapping, Indexes, Artificial intelligence (AI), satellite remote sensing BibRef

He, W.[Wei], Yokoya, N.[Naoto], Yuan, X.[Xin],
Fast Hyperspectral Image Recovery of Dual-Camera Compressive Hyperspectral Imaging via Non-Iterative Subspace-Based Fusion,
IP(30), 2021, pp. 7170-7183.
IEEE DOI 2108
Image reconstruction, Hyperspectral imaging, Sensors, Image coding, Particle measurements, Atmospheric measurements, fusion BibRef

Cao, M.[Meng], Bao, W.X.[Wen-Xing], Qu, K.[Kewen],
Hyperspectral Super-Resolution Via Joint Regularization of Low-Rank Tensor Decomposition,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Jameel, S.K.[Samer Kais], Majidpour, J.[Jafar],
Generating Spectrum Images from Different Types: Visible, Thermal, and Infrared Based on Autoencoder Architecture (GVTI-AE),
IJIG(22), No. 1 2022, pp. 2250005.
DOI Link 2202
BibRef

Rodríguez-Suárez, B.[Brais], Quesada-Barriuso, P.[Pablo], Argüello, F.[Francisco],
Design of CGAN Models for Multispectral Reconstruction in Remote Sensing,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wang, L.X.[Li-Xia], Sole, A.[Aditya], Hardeberg, J.Y.[Jon Yngve],
Densely Residual Network with Dual Attention for Hyperspectral Reconstruction from RGB Images,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Stepcenkov, S.[Sergej], Wilhelm, T.[Thorsten], Wöhler, C.[Christian],
Learning the Link between Albedo and Reflectance: Machine Learning-Based Prediction of Hyperspectral Bands from CTX Images,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Yang, K.X.[Kai-Xiang], Luo, Y.M.[You-Ming], Li, M.Y.[Meng-Yao], Zhong, S.Y.[Shou-Yi], Liu, Q.[Qiang], Li, X.H.[Xiu-Hong],
Reconstruction of Sentinel-2 Image Time Series Using Google Earth Engine,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Xie, S.C.[Shi-Cheng], Wang, S.[Shun], Song, C.M.[Chuan-Ming], Wang, X.H.[Xiang-Hai],
Hyperspectral Image Reconstruction Based on Spatial-Spectral Domains Low-Rank Sparse Representation,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Peng, M.Y.[Ming-Yuan], Li, G.Y.[Guo-Yuan], Zhou, X.Q.[Xiao-Qing], Ma, C.[Chen], Zhang, L.[Lifu], Zhang, X.[Xia], Shang, K.[Kun],
A Registration-Error-Resistant Swath Reconstruction Method of ZY1-02D Satellite Hyperspectral Data Using SRE-ResNet,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
Hyperspectral swath is narrower than multi-spectral. BibRef

Huang, F.[Feng], Chen, Y.T.[Ya-Ting], Wang, X.S.[Xue-Song], Wang, S.[Shu], Wu, X.Y.[Xian-Yu],
Spectral Clustering Super-Resolution Imaging Based on Multispectral Camera Array,
IP(32), 2023, pp. 1257-1271.
IEEE DOI 2302
Imaging, Apertures, Cameras, Superresolution, Multispectral imaging, Image reconstruction, Band-pass filters, adaptive kernel BibRef

Mohamed, A.[Ali], Emam, A.[Ashraf], Zoheir, B.[Basem],
SAM-HIT: A Simulated Annealing Multispectral to Hyperspectral Imagery Data Transformation,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Xiao, S.M.[Shu-Ming], Zhang, Y.[Ye], Chang, X.[Xuling], Xu, J.J.[Jia-Jia],
Compressive sensing reconstruction of hyperspectral images based on codec space-spectrum joint dense residual network,
IET-IPR(17), No. 3, 2023, pp. 916-931.
DOI Link 2303
BibRef

Mohan, D.[Divya], Aravinth, J., Rajendran, S.[Sankaran],
Reconstruction of Compressed Hyperspectral Image Using SqueezeNet Coupled Dense Attentional Net,
RS(15), No. 11, 2023, pp. 2734.
DOI Link 2306
BibRef

Ran, R.[Ran], Deng, L.J.[Liang-Jian], Jiang, T.X.[Tai-Xiang], Hu, J.F.[Jin-Fan], Chanussot, J.[Jocelyn], Vivone, G.[Gemine],
GuidedNet: A General CNN Fusion Framework via High-Resolution Guidance for Hyperspectral Image Super-Resolution,
Cyber(53), No. 7, July 2023, pp. 4148-4161.
IEEE DOI 2307
Image reconstruction, Task analysis, Superresolution, Pansharpening, Hyperspectral imaging, Spatial resolution, Training, single-image super-resolution (SISR) BibRef

Palsson, B.[Burkni], Ulfarsson, M.O.[Magnus O.], Sveinsson, J.R.[Johannes R.],
Synthesis of Synthetic Hyperspectral Images with Controllable Spectral Variability Using a Generative Adversarial Network,
RS(15), No. 16, 2023, pp. 3919.
DOI Link 2309
BibRef

Zhao, E.[Enyu], Qu, N.[Nianxin], Wang, Y.[Yulei], Gao, C.X.[Cai-Xia],
Spectral Reconstruction from Thermal Infrared Multispectral Image Using Convolutional Neural Network and Transformer Joint Network,
RS(16), No. 7, 2024, pp. 1284.
DOI Link 2404
BibRef


Dong, X.Y.[Xiao-Yi], Zhu, Y.[Yu], Li, C.H.[Cheng-Hua], Wang, P.S.[Pei-Song], Cheng, J.[Jian],
Rispnet: A Network for Reversed Image Signal Processing,
AIM22(445-457).
Springer DOI 2304
RGB to RAW data. BibRef

Zou, B.[Beiji], Zhang, Y.[Yue],
Learned Reverse ISP with Soft Supervision,
AIM22(489-506).
Springer DOI
WWW Link. 2304
BibRef

Xu, R.K.[Rui-Kang], Yao, M.[Mingde], Chen, C.[Chang], Wang, L.Z.[Li-Zhi], Xiong, Z.W.[Zhi-Wei],
Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation,
MIPI22(78-94).
Springer DOI 2304
BibRef

Liu, S.[Song], Li, H.W.[Hai-Wei], Zhang, G.[Geng], Hu, B.L.[Bing-Liang], Chen, J.Y.[Jun-Yu],
Using Hyperspectral Reconstruction for Multispectral Images Change Detection,
ICIVC22(183-188)
IEEE DOI 2301
Training, Image segmentation, Reconstruction algorithms, Image reconstruction, Hyperspectral imaging, change detection, hyperspectral image reconstruction BibRef

Huang, J.R.[Jun-Ru], Sun, Y.[Yubao], Wen, J.X.[Jia-Xuan], Liu, Q.S.[Qing-Shan],
Transformer-based Residual Network for Hyperspectral Snapshot Compressive Reconstruction,
ICPR22(5075-5081)
IEEE DOI 2212
Image coding, Imaging, Reconstruction algorithms, Transformer cores, Transformers, Convolutional neural networks, Transformer joint residual block BibRef

Shukla, A.[Ankit], Upadhyay, A.[Avinash], Sharma, M.[Manoj], Chinnusamy, V.[Viswanathan], Kumar, S.[Sudhir],
High-Resolution NIR Prediction from RGB Images: Application to Plant Phenotyping,
ICIP22(4058-4062)
IEEE DOI 2211
Learning systems, Spectroscopy, Image registration, Plants (biology), Superresolution, Predictive models, Pix-to-pix BibRef

Zhang, X.Y.[Xuan-Yu], Zhang, Y.B.[Yong-Bing], Xiong, R.Q.[Rui-Qin], Sun, Q.[Qilin], Zhang, J.[Jian],
HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imaging,
CVPR22(17511-17520)
IEEE DOI 2210
Photography, Deep learning, Correlation, Fuses, Sensors, Pattern recognition, Iterative methods, Low-level vision, Computational photography BibRef

Cai, Y.H.[Yuan-Hao], Lin, J.[Jing], Hu, X.W.[Xiao-Wan], Wang, H.Q.[Hao-Qian], Yuan, X.[Xin], Zhang, Y.[Yulun], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction,
ECCV22(XVII:686-704).
Springer DOI 2211
BibRef
Earlier:
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction,
CVPR22(17481-17490)
IEEE DOI 2210
Photography, Computational modeling, Memory management, Apertures, Transformers, Pattern recognition, Low-level vision, Computational photography BibRef

Cai, Y.H.[Yuan-Hao], Lin, J.[Jing], Lin, Z.[Zudi], Wang, H.Q.[Hao-Qian], Zhang, Y.[Yulun], Pfister, H.[Hanspeter], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction,
NTIRE22(744-754)
IEEE DOI 2210
Conferences, Memory management, Transformers, Image restoration, Data mining, Convolutional neural networks BibRef

Agarla, M.[Mirko], Bianco, S.[Simone], Buzzelli, M.[Marco], Celona, L.[Luigi], Schettini, R.[Raimondo],
Fast-n-Squeeze: towards real-time spectral reconstruction from RGB images,
NTIRE22(1131-1138)
IEEE DOI 2210
Training, Measurement, Image resolution, Neural networks, Real-time systems, Pattern recognition, Convolutional neural networks BibRef

Zhu, Z.Y.[Zhi-Yu], Liu, H.[Hui], Hou, J.H.[Jun-Hui], Zeng, H.Q.[Huan-Qiang], Zhang, Q.F.[Qing-Fu],
Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild,
ICCV21(2259-2268)
IEEE DOI 2203
Training, Degradation, Visualization, Semantics, Estimation, Reconstruction algorithms, Cameras, Computational photography, Image and video synthesis BibRef

Li, K.[Ke], Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Hyperspectral Image Super-Resolution with RGB Image Super-Resolution as an Auxiliary Task,
WACV22(4039-4048)
IEEE DOI 2202
Training, Deep learning, Convolutional codes, Superresolution, Neural networks, Training data, Multitasking, Infrared/Spectral Imaging Image Processing BibRef

Yang, L.T.[Liu-Tao], Li, Z.N.[Zhong-Nian], Pei, Z.X.[Zong-Xiang], Zhang, D.Q.[Dao-Qiang],
Fs-Net: Filter Selection Network for Hyperspectral Reconstruction,
ICIP21(2933-2937)
IEEE DOI 2201
Optical filters, Training, Artificial neural networks, Computational complexity, Image reconstruction, Optimization, sparse regularization BibRef

Kinoshita, Y.[Yuma], Kiya, H.[Hitoshi],
Separated-Spectral-Distribution Estimation Based on Bayesian Inference with Single RGB Camera,
ICIP21(1379-1383)
IEEE DOI 2201
Reflectivity, Sensitivity, Image color analysis, Estimation, Lighting, Cameras, Robustness, Bayesian inference, spectral distribution BibRef

Yamawaki, K.[Kazuhiro], Yorimoto, K.[Kouhei], Han, X.H.[Xian-Hua],
Hyperspectral Reconstruction Using Auxiliary RGB Learning from a Snapshot Image,
ICIP22(186-190)
IEEE DOI 2211
BibRef
Earlier: A2, A3, Only:
HyperMixNet: Hyperspectral Image Reconstruction with Deep Mixed Network from a Snapshot Measurement,
PBDL21(1184-1193)
IEEE DOI 2112
Training, Image sensors, Image coding, Sensitivity, Sensors, Task analysis, Hyperspectral image reconstruction, intermediate feature fusion. Convolutional codes, Benchmark testing, Visual effects, Loss measurement BibRef

Aslahishahri, M.[Masoomeh], Stanley, K.G.[Kevin G.], Duddu, H.[Hema], Shirtliffe, S.[Steve], Vail, S.[Sally], Bett, K.[Kirstin], Pozniak, C.[Curtis], Stavness, I.[Ian],
From RGB to NIR: Predicting of near infrared reflectance from visible spectrum aerial images of crops,
CVPPA21(1312-1322)
IEEE DOI 2112
Reflectivity, Training, Spectroscopy, Software algorithms, Crops, Cameras, Radiometry BibRef

Zhang, S.P.[Shi-Peng], Wang, L.Z.[Li-Zhi], Zhang, L.[Lei], Huang, H.[Hua],
Learning Tensor Low-Rank Prior for Hyperspectral Image Reconstruction,
CVPR21(12001-12010)
IEEE DOI 2111
Deep learning, Tensors, Correlation, Iterative algorithms, Pattern recognition BibRef

Sun, B.[Bo], Yan, J.C.[Jun-Chi], Zhou, X.[Xiao], Zheng, Y.Q.[Yin-Qiang],
Tuning IR-cut Filter for Illumination-aware Spectral Reconstruction from RGB,
CVPR21(84-93)
IEEE DOI 2111
Reflectivity, Deep learning, Lighting, Reconstruction algorithms, Filtering algorithms, Cameras BibRef

Yamawaki, K.[Kazuhiro], Han, X.H.[Xian-Hua],
Lightweight Hyperspectral Image Reconstruction Network with Deep Feature Hallucination,
MLCSA22(170-184).
Springer DOI 2307
BibRef

Kohei, Y.[Yorimoto], Han, X.H.[Xian-Hua],
Deep Residual Attention Network for Hyperspectral Image Reconstruction,
ICPR21(8547-8553)
IEEE DOI 2105
Deep learning, Inverse problems, Benchmark testing, Reconstruction algorithms, Visual effects, Convolutional neural networks BibRef

Simonetto, A.[Adriano], Zanuttigh, P.[Pietro], Parret, V.[Vincent], Sartor, P.[Piergiorgio], Gatto, A.[Alexander],
Semi-supervised Deep Learning Techniques for Spectrum Reconstruction,
ICPR21(7767-7774)
IEEE DOI 2105
Deep learning, Training, Databases, Transfer learning, Estimation, Training data, Semisupervised learning BibRef

Cheng, N.[Niankai], Huang, H.[Hua], Zhang, L.[Lei], Wang, L.Z.[Li-Zhi],
Snapshot Hyperspectral Imaging Based on Weighted High-order Singular Value Regularization,
ICPR21(1267-1274)
IEEE DOI 2105
Solid modeling, Tensors, Correlation, Reconstruction algorithms, Pattern recognition, Optimization BibRef

Peng, H.[Hao], Chen, X.M.[Xiao-Mei], Zhao, J.[Jie],
Residual Pixel Attention Network for Spectral Reconstruction from RGB Images,
NTIRE20(2012-2020)
IEEE DOI 2008
Image reconstruction, Hyperspectral imaging, Task analysis, Spatial resolution, Convolution BibRef

Fubara, B.J., Sedky, M., Dyke, D.,
RGB to Spectral Reconstruction via Learned Basis Functions and Weights,
NTIRE20(1984-1993)
IEEE DOI 2008
Hyperspectral imaging, Image reconstruction, Machine learning, Training, Image color analysis, Sensors BibRef

Lin, Y., Finlayson, G.D.,
Physically Plausible Spectral Reconstruction from RGB Images,
NTIRE20(2257-2266)
IEEE DOI 2008
Image color analysis, Image reconstruction, Hyperspectral imaging, Sensitivity, Cameras, Computational modeling BibRef

Rout, L., Misra, I., Moorthi, S.M., Dhar, D.,
S2A: Wasserstein GAN with Spatio-Spectral Laplacian Attention for Multi-Spectral Band Synthesis,
EarthVision20(727-736)
IEEE DOI 2008
Spatial resolution, Signal resolution, Generators, Remote sensing, Laplace equations BibRef

Zhao, Y., Po, L., Yan, Q., Liu, W., Lin, T.,
Hierarchical Regression Network for Spectral Reconstruction from RGB Images,
NTIRE20(1695-1704)
IEEE DOI 2008
Image reconstruction, Hyperspectral imaging, Training, Cameras, Image resolution BibRef

Li, J., Wu, C., Song, R., Li, Y., Liu, F.,
Adaptive Weighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images,
NTIRE20(1894-1903)
IEEE DOI 2008
Image reconstruction, Adaptive systems, Adaptation models, Correlation, Hyperspectral imaging, Cascading style sheets, Task analysis BibRef

Nie, S.J.[Shi-Jie], Gu, L.[Lin], Zheng, Y.Q.[Yin-Qiang], Lam, A.[Antony], Ono, N.[Nobutaka], Sato, I.[Imari],
Deeply Learned Filter Response Functions for Hyperspectral Reconstruction,
CVPR18(4767-4776)
IEEE DOI 1812
Image reconstruction, Hyperspectral imaging, Cameras, Convolution, Hardware BibRef

Ma, J.W.[Jia-Wei], Liu, X.Y.[Xiao-Yang], Shou, Z.[Zheng], Yuan, X.[Xin],
Deep Tensor ADMM-Net for Snapshot Compressive Imaging,
ICCV19(10222-10231)
IEEE DOI 2004
computational complexity, data compression, decoding, gradient methods, image coding, image reconstruction, noise figure 2.5 dB BibRef

Wu, J., Aeschbacher, J., Timofte, R.,
In Defense of Shallow Learned Spectral Reconstruction from RGB Images,
PBVDL17(471-479)
IEEE DOI 1802
Dictionaries, Hyperspectral imaging, Image reconstruction, Spatial resolution, Training BibRef

Jia, Y.[Yan], Zheng, Y.Q.[Yin-Qiang], Gu, L.[Lin], Subpa-Asa, A.[Art], Lam, A.[Antony], Sato, Y.[Yoichi], Sato, I.[Imari],
From RGB to Spectrum for Natural Scenes via Manifold-Based Mapping,
ICCV17(4715-4723)
IEEE DOI 1802
Reconstruct hyperspectral data from only RGB. cameras, geophysical image processing, image classification, image colour analysis, image reconstruction, image sensors, BibRef

Hoffer, N.N.[Nirit Nussbaum], Michaeli, T.[Tomer],
Multispectral Reconstruction From Reference Objects in the Scene,
PBDL19(4350-4358)
IEEE DOI 2004
cameras, hyperspectral imaging, image reconstruction, inverse problems, optical transfer function, Color reconstruction BibRef

Han, X., Shi, B., Zheng, Y.,
Residual HSRCNN: Residual Hyper-Spectral Reconstruction CNN from an RGB Image,
ICPR18(2664-2669)
IEEE DOI 1812
Spatial resolution, Image reconstruction, Signal resolution, Computer architecture, Cameras, Visualization BibRef

Koundinya, S., Sharma, H., Sharma, M., Upadhyay, A., Manekar, R., Mukhopadhyay, R., Karmakar, A., Chaudhury, S.,
2D-3D CNN Based Architectures for Spectral Reconstruction from RGB Images,
Restoration18(957-9577)
IEEE DOI 1812
Hyperspectral imaging, Image reconstruction, Kernel, Feature extraction, Image resolution BibRef

Stiebei, T., Köppers, S., Seltsam, P., Merhof, D.,
Reconstructing Spectral Images from RGB-Images Using a Convolutional Neural Network,
Restoration18(1061-10615)
IEEE DOI 1812
Pattern recognition. BibRef

Li, H., Xiong, Z., Shi, Z., Wang, L., Liu, D., Wu, F.,
HSVCNN: CNN-Based Hyperspectral Reconstruction from RGB Videos,
ICIP18(3323-3327)
IEEE DOI 1809
Videos, Image reconstruction, Motion compensation, Correlation, Hyperspectral imaging, Optical imaging, Adaptive optics, temporal-adaptive fusion BibRef

Shi, Z., Chen, C., Xiong, Z., Liu, D., Wu, F.,
HSCNN+: Advanced CNN-Based Hyperspectral Recovery from RGB Images,
Restoration18(1052-10528)
IEEE DOI 1812
Hyperspectral imaging, Image reconstruction, Task analysis, Spatial resolution, Cameras BibRef

Xiong, Z., Shi, Z., Li, H., Wang, L., Liu, D., Wu, F.,
HSCNN: CNN-Based Hyperspectral Image Recovery from Spectrally Undersampled Projections,
PBVDL17(518-525)
IEEE DOI 1802
Hyperspectral imaging, Image reconstruction, Machine learning, Optical filters, Spatial resolution BibRef

Blasinski, H.[Henryk], Farrell, J.[Joyce], Wandell, B.[Brian],
An iterative algorithm for spectral estimation with spatial smoothing,
ICIP15(936-940)
IEEE DOI 1512
ADMM, Multispectral imaging, spectral reconstruction BibRef

Parmar, M.[Manu], Lansel, S.[Steven], Wandell, B.A.[Brian A.],
Spatio-spectral reconstruction of the multispectral datacube using sparse recovery,
ICIP08(473-476).
IEEE DOI 0810
BibRef

Connah, D., Hardeberg, J.Y., Westland, S.,
Comparison of linear spectral reconstruction methods for multispectral imaging,
ICIP04(III: 1497-1500).
IEEE DOI 0505
BibRef

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Restoration from Blurred Images, Motion Blur .


Last update:May 6, 2024 at 15:50:14