A novel fusion paradigm for multi-channel image denoising

Dabov, 2007, Image denoising by sparse 3-d transform-domain collaborative filtering, IEEE Trans. Image Process., 16, 2080, 10.1109/TIP.2007.901238 Yang, 2009, A fast algorithm for edge-preserving variational multichannel image restoration, SIAM J. Imag. Sci., 2, 569, 10.1137/080730421 Yuan, 2012, Hyperspectral image denoising employing a spectral–spatial adaptive total variation model, IEEE Trans. Geosci. Remote Sens., 50, 3660, 10.1109/TGRS.2012.2185054 Fan, 2018, Spatial–spectral total variation regularized low-rank tensor decomposition for hyperspectral image denoising, IEEE Trans. Geosci. Remote Sens., 56, 6196, 10.1109/TGRS.2018.2833473 Mairal, 2008, Sparse representation for color image restoration, IEEE Trans. Image Process., 17, 53, 10.1109/TIP.2007.911828 Maggioni, 2013, Nonlocal transform-domain filter for volumetric data denoising and reconstruction, IEEE Trans. Image Process., 22, 119, 10.1109/TIP.2012.2210725 Zhao, 2015, Hyperspectral image denoising via sparse representation and low-rank constraint, IEEE Trans. Geosci. Remote Sens., 53, 296, 10.1109/TGRS.2014.2321557 Zhang, 2014, Hyperspectral image restoration using low-rank matrix recovery, IEEE Trans. Geosci. Remote Sens., 52, 4729, 10.1109/TGRS.2013.2284280 He, 2016, Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration, IEEE Trans. Geosci. Remote Sens., 54, 178, 10.1109/TGRS.2015.2452812 J. Xu, L. Zhang, D. Zhang, X. Feng, Multi-channel weighted nuclear norm minimization for real color image denoising, in: Proc. IEEE Int. Conf. Comput. Vis., 2017, pp. 1105–1113. Fan, 2017, Hyperspectral image denoising with superpixel segmentation and low-rank representation, Inform. Sci., 397–398, 48, 10.1016/j.ins.2017.02.044 Liu, 2019, Speckle suppression based on weighted nuclear norm minimization and grey theory, IEEE Trans. Geosci. Remote Sens., 57, 2700, 10.1109/TGRS.2018.2876339 Huang, 2020, Spatial-spectral weighted nuclear norm minimization for hyperspectral image denoising, Neurocomputing, 399, 271, 10.1016/j.neucom.2020.01.103 Renard, 2008, Denoising and dimensionality reduction using multilinear tools for hyperspectral images, IEEE Geosci. Remote Sens. Lett., 5, 138, 10.1109/LGRS.2008.915736 Y. Peng, D. Meng, Z. Xu, C. Gao, Y. Yang, B. Zhang, Decomposable nonlocal tensor dictionary learning for multispectral image denoising, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit, 2014, pp. 2949–2956. Y. Chang, L. Yan, S. Zhong, Hyper-Laplacian regularized unidirectional low-rank tensor recovery for multispectral image denoising, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit, 2017, pp. 5901–5909. Xie, 2018, Kronecker-basis-representation based tensor sparsity and its applications to tensor recovery, IEEE Trans. Pattern Anal. Mach. Intell., 40, 1888, 10.1109/TPAMI.2017.2734888 Xie, 2017, Hyperspectral imagery denoising by deep learning with trainable nonlinearity function, IEEE Geosci. Remote Sens. Lett., 14, 1963, 10.1109/LGRS.2017.2743738 Chang, 2019, HSI-Denet: Hyperspectral image restoration via convolutional neural network, IEEE Trans. Geosci. Remote Sens., 57, 667, 10.1109/TGRS.2018.2859203 Yuan, 2019, Hyperspectral image denoising employing a spatial–spectral deep residual convolutional neural network, IEEE Trans. Geosci. Remote Sens., 57, 1205, 10.1109/TGRS.2018.2865197 Y. Kim, J.W. Soh, G.Y. Park, N.I. Cho, Transfer learning from synthetic to real-noise denoising with adaptive instance normalization, in: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit, 2020, pp. 3479–3489. Lin, 2020, Hyperspectral image denoising via matrix factorization and deep prior regularization, IEEE Trans. Image Process., 29, 565, 10.1109/TIP.2019.2928627 Maffei, 2020, A single model CNN for hyperspectral image denoising, IEEE Trans. Geosci. Remote Sens., 58, 2516, 10.1109/TGRS.2019.2952062 Chen, 2011, Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage, IEEE Trans. Geosci. Remote Sens., 49, 973, 10.1109/TGRS.2010.2075937 Ophir, 2011, Multi-scale dictionary learning using wavelets, IEEE J. Sel. Topics Signal Process., 5, 1014, 10.1109/JSTSP.2011.2155032 Yan, 2013, Nonlocal hierarchical dictionary learning using wavelets for image denoising, IEEE Trans. Image Process., 22, 4689, 10.1109/TIP.2013.2277813 Bertalmío, 2014, Denoising an image by denoising its curvature image, SIAM J. Imag. Sci., 7, 187, 10.1137/120901246 Ghimpeţeanu, 2016, A decomposition framework for image denoising algorithms, IEEE Trans. Image Process., 25, 388, 10.1109/TIP.2015.2498413 Zhuang, 2018, Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 11, 730, 10.1109/JSTARS.2018.2796570 W. He, Q. Yao, C. Li, N. Yokoya, Q. Zhao, Non-local meets global: An integrated paradigm for hyperspectral denoising, in: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit, 2019, pp. 6861–6870. Zhang, 2010, Two-stage image denoising by principal component analysis with local pixel grouping, Pattern Recognit., 43, 153, 10.1016/j.patcog.2009.09.023 S. Gu, L. Zhang, W. Zuo, X. Feng, Weighted nuclear norm minimization with application to image denoising, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit, 2014, pp. 2862–2869. Zhang, 2017, Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising, IEEE Trans. Image Process., 26, 3142, 10.1109/TIP.2017.2662206 Goyal, 2020, Image denoising review: From classical to state-of-the-art approaches, Inf. Fusion, 55, 220, 10.1016/j.inffus.2019.09.003 Priego, 2017, 4DCAF: A temporal approach for denoising hyperspectral image sequences, Pattern Recognit., 72, 433, 10.1016/j.patcog.2017.07.023 Su, 2020, Multi-band weighted lp norm minimization for image denoising, Inform. Sci., 537, 162, 10.1016/j.ins.2020.05.049 Wang, 2014, Fusion of multispectral and panchromatic images via sparse representation and local autoregressive model, Inf. Fusion, 20, 73, 10.1016/j.inffus.2013.11.004 Wei, 2015, Hyperspectral and multispectral image fusion based on a sparse representation, IEEE Trans. Geosci. Remote Sens., 53, 3658, 10.1109/TGRS.2014.2381272 Ghassemian, 2016, A review of remote sensing image fusion methods, Inf. Fusion, 32, 75, 10.1016/j.inffus.2016.03.003 Ma, 2016, Infrared and visible image fusion via gradient transfer and total variation minimization, Inf. Fusion, 31, 100, 10.1016/j.inffus.2016.02.001 Liu, 2018, Deep learning for pixel-level image fusion: Recent advances and future prospects, Inf. Fusion, 42, 158, 10.1016/j.inffus.2017.10.007 Meng, 2019, Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: Practical discussion and challenges, Inf. Fusion, 46, 102, 10.1016/j.inffus.2018.05.006 Wang, 2004, Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., 13, 600, 10.1109/TIP.2003.819861 Yasuma, 2010, Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum, IEEE Trans. Image Process., 19, 2241, 10.1109/TIP.2010.2046811 A. Chakrabarti, T. Zickler, Statistics of real-world hyperspectral images, in: Proc. CVPR, 2011, pp. 193–200. Bioucas-Dias, 2008, Hyperspectral subspace identification, IEEE Trans. Geosci. Remote Sens., 46, 2435, 10.1109/TGRS.2008.918089 Zhang, 2018, FFDNet: Toward a fast and flexible solution for CNN-based image denoising, IEEE Trans. Image Process., 27, 4608, 10.1109/TIP.2018.2839891 Kong, 2019, Color image and multispectral image denoising using block diagonal representation, IEEE Trans. Image Process., 28, 4247, 10.1109/TIP.2019.2907478 Peng, 2020, Enhanced 3DTV regularization and its applications on HSI denoising and compressed sensing, IEEE Trans. Image Process., 29, 7889, 10.1109/TIP.2020.3007840