5.3.9.4 Noise Removal, Adaptive, Non-linear Techniques

Wang, Q., Zhang, X., Wu, Y., Tang, L., Zha, Z.,
Nonconvex Weighted L_p Minimization Based Group Sparse Representation Framework for Image Denoising,
SPLetters(24), No. 11, November 2017, pp. 1686-1690.
IEEE DOI 1710
Dictionaries, Image denoising, Minimization, Noise measurement, Noise reduction, Sparse matrices, Adaptive patch search (APS), generalized soft-thresholding (GST) algorithm, group sparsity, image denoising, weighted, L_p, minimization BibRef

Chen, Y., Cao, X., Zhao, Q., Meng, D., Xu, Z.,
Denoising Hyperspectral Image With Non-i.i.d. Noise Structure,
Cyber(48), No. 3, March 2018, pp. 1054-1066.
IEEE DOI 1802
Adaptation models, Algorithm design and analysis, Correlation, Encoding, Noise reduction, Robustness, Tensile stress, non independent and identically distributed (i.i.d.) noise modeling BibRef

Isogawa, K., Ida, T., Shiodera, T., Takeguchi, T.,
Deep Shrinkage Convolutional Neural Network for Adaptive Noise Reduction,
SPLetters(25), No. 2, February 2018, pp. 224-228.
IEEE DOI 1802
convolution, image denoising, image restoration, image segmentation, learning (artificial intelligence), supervised learning BibRef

Thirumalaisamy, M.R., Ansell, P.J.,
Fast and Adaptive Empirical Mode Decomposition for Multidimensional, Multivariate Signals,
SPLetters(25), No. 10, October 2018, pp. 1550-1554.
IEEE DOI 1810
Hilbert transforms, multidimensional signal processing, signal denoising, multivariate EMD, multivariate signals, multivariate signal BibRef

Treece, G.,
Morphology-Based Noise Reduction: Structural Variation and Thresholding in the Bitonic Filter,
IP(29), No. 1, 2020, pp. 336-350.
IEEE DOI 1910
adaptive filters, image denoising, image filtering, image segmentation, morphology-based noise reduction, edge preservation BibRef

Narasimha, C., Rao, A.N.[A. Nagaraja],
Integrating Taylor-Krill herd-based SVM to fuzzy-based adaptive filter for medical image denoising,
IET-IPR(14), No. 3, 28 February 2020, pp. 442-450.
DOI Link 2002
BibRef

Zhang, Q.A.[Qi-Ang], Yuan, Q.Q.[Qiang-Qiang], Li, J.[Jie], Sun, F.J.[Fu-Jun], Zhang, L.P.[Liang-Pei],
Deep spatio-spectral Bayesian posterior for hyperspectral image non-i.i.d. noise removal,
PandRS(164), 2020, pp. 125-137.
Elsevier DOI 2005
Non-i.i.d. noise, Noise estimation and removal, Spatio-spectral, Bayesian posterior, Convolutional neural network BibRef

Viswanath, S., Ghulyani, M., de Beco, S., Dahan, M., Arigovindan, M.,
Image Restoration by Combined Order Regularization With Optimal Spatial Adaptation,
IP(29), 2020, pp. 6315-6329.
IEEE DOI 2006
Image restoration, Minimization, TV, Image reconstruction, Magnetic resonance imaging, Microscopy, total internal reflection fluorescence microscopy BibRef

Rotker, K.[Keren], Ben Bashat, D.[Dafna], Bronstein, A.M.[Alex M.],
Overparameterized Models for Vector Fields,
SIIMS(13), No. 3, 2020, pp. 1386-1414.
DOI Link 2010
BibRef

Yu, J.[Jimin], Chen, L.[Long], Zhou, S.B.[Shang-Bo], Wang, L.M.[Li-Min], Li, H.T.[Han-Tao], Huang, S.[Saiao],
Adaptive image denoising for speckle noise images based on fuzzy logic,
IJIST(30), No. 4, 2020, pp. 1132-1142.
DOI Link 2011
convolution template, fuzzy logic, image denoising, mask template, membership function BibRef

Adcock, B.[Ben], Dexter, N.[Nick], Xu, Q.H.[Qing-Hong],
Improved Recovery Guarantees and Sampling Strategies for TV Minimization in Compressive Imaging,
SIIMS(14), No. 3, 2021, pp. 1149-1183.
DOI Link 2108
BibRef

Kumar, A.[Ahlad], Ahmad, M.O.[M. Omair], Swamy, M.N.S.,
Image Denoising Based on Fractional Gradient Vector Flow and Overlapping Group Sparsity as Priors,
IP(30), 2021, pp. 7527-7540.
IEEE DOI 2109
Noise reduction, Image restoration, Optimization, Image edge detection, Image denoising, TV, alternating direction method of multipliers and split Bregman optimization BibRef

Sun, Y.[Yubao], Chen, X.[Xunhao], Kankanhalli, M.S.[Mohan S.], Liu, Q.S.[Qing-Shan], Li, J.X.[Jun-Xia],
Video Snapshot Compressive Imaging Using Residual Ensemble Network,
CirSysVideo(32), No. 9, September 2022, pp. 5931-5943.
IEEE DOI 2209
Image reconstruction, Correlation, Imaging, Spatiotemporal phenomena, Reconstruction algorithms, spatiotemporal correlations BibRef

Xu, W.[Wei], Zhu, Q.[Qing], Qi, N.[Na], Chen, D.P.[Dong-Pan],
Deep Sparse Representation Based Image Restoration With Denoising Prior,
CirSysVideo(32), No. 10, October 2022, pp. 6530-6542.
IEEE DOI 2210
Noise reduction, Dictionaries, Task analysis, Image restoration, Deep learning, Optimization, TV, Deep neural network, sparse representation BibRef

Kim, C.J.[Chang-Jin], Kim, T.H.[Tae Hyun], Baik, S.[Sungyong],
LAN: Learning to Adapt Noise for Image Denoising,
CVPR24(25193-25202)
IEEE DOI 2410
Training, Adaptation models, Noise, Noise reduction, Deep architecture BibRef

Herbreteau, S.[Sébastien], Kervrann, C.[Charles],
Towards a Unified View of Unsupervised Non-Local Methods for Image Denoising: The NL-Ridge Approach,
ICIP22(3376-3380)
IEEE DOI 2211
Deep learning, Adaptation models, Estimation, Minimization, Noise measurement, Image denoising, Patch-based image denoising, Ridge regression BibRef

Brokman, J.[Jonathan], Gilboa, G.[Guy],
Nonlinear Spectral Processing of Shapes via Zero-homogeneous Flows,
SSVM21(40-51).
Springer DOI 2106
BibRef

Pronina, V.[Valeriya], Kokkinos, F.[Filippos], Dylov, D.V.[Dmitry V.], Lefkimmiatis, S.[Stamatios],
Microscopy Image Restoration with Deep Wiener-Kolmogorov Filters,
ECCV20(XX:185-201).
Springer DOI 2011
BibRef