{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T03:33:31Z","timestamp":1768793611720,"version":"3.49.0"},"reference-count":87,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901309"],"award-info":[{"award-number":["61901309"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mixed (random and stripe) noise will cause serious degradation of optical remotely sensed image quality, making it hard to analyze their contents. In order to remove such noise, various inverse problems are usually constructed with different priors, which can be solved by either model-based optimization methods or discriminative learning methods. However, they have their own drawbacks, such as the former methods are flexible but are time-consuming for the pursuit of good performance; while the later methods are fast but are limited for extensive applications due to their specialized tasks. To fast obtain pleasing results with combination of their merits, in this paper, we propose a novel denoising strategy, namely, Dual Denoiser Driven Convolutional Neural Networks (D3CNNs), to remove both random and stripe noise. The D3CNNs includes the following two key parts: one is that two auxiliary variables respective for the denoised image and the stripe noise are introduced to reformulate the inverse problem as a constrained optimization problem, which can be iteratively solved by employing the alternating direction method of multipliers (ADMM). The other is that the U-shape network is used for the denoised auxiliary variable while the residual CNN (RCNN) for the stripe auxiliary variable. The subjectively and objectively comparable results of experiments on both synthetic and real-world remotely sensed images verify that the proposed method is effective and is even better than the state-of-the-arts.<\/jats:p>","DOI":"10.3390\/rs15020443","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T03:11:02Z","timestamp":1673493062000},"page":"443","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["D3CNNs: Dual Denoiser Driven Convolutional Neural Networks for Mixed Noise Removal in Remotely Sensed Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3128-2405","authenticated-orcid":false,"given":"Zhenghua","family":"Huang","sequence":"first","affiliation":[{"name":"School of Information and Artificial Intelligence, Nanchang Institute of Science and Technology, Nanchang 330108, China"},{"name":"Artificial Intelligence School, Wuchang University of Technology, Wuhan 430223, China"},{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8834-9060","authenticated-orcid":false,"given":"Zifan","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7113-731X","authenticated-orcid":false,"given":"Zhicheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}]},{"given":"Xi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Artificial Intelligence, Nanchang Institute of Science and Technology, Nanchang 330108, China"},{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}]},{"given":"Biyun","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}]},{"given":"Yaozong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}]},{"given":"Hao","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Wuhan Donghu University, Wuhan 430212, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112045","DOI":"10.1016\/j.rse.2020.112045","article-title":"Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning","volume":"250","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/TGRS.2017.2756911","article-title":"Large-scale remote sensing image retrieval by deep hashing neural networks","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","first-page":"145","article-title":"Robust deep alignment network with remote sensing knowledge graph for zero-shot and generalized zero-shot remote sensing image scene classification","volume":"179","author":"Li","year":"2021","journal-title":"ISPRS JPRS"},{"key":"ref_4","first-page":"170","article-title":"DKDFN: Domain knowledge-guided deep collaborative fusion network for multimodal unitemporal remote sensing land cover classification","volume":"186","author":"Li","year":"2022","journal-title":"ISPRS JPRS"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, W., Huang, X., Gao, Z., Li, S., He, T., and Zhang, Y. (2022). Mfvnet: Deep adaptive fusion network with multiple field-of-views for remote sensing image semantic segmentation. Sci. China Inf. Sci.","DOI":"10.1007\/s11432-022-3599-y"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1109\/LGRS.2018.2796604","article-title":"Progressive dual-domain filter for enhancing and denoising optical remote sensing images","volume":"15","author":"Huang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2880","DOI":"10.1109\/TGRS.2019.2957153","article-title":"Toward universal stripe removal via wavelet-based deep convolutional neural network","volume":"58","author":"Chang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","article-title":"Nonlinear total variation based noise removal algorithms","volume":"60","author":"Rudin","year":"1992","journal-title":"Physica D"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Han, L., Zhao, Y., Lv, H., Zhang, Y., Liu, H., and Bi, G. (2022). Remote Sensing Image Denoising Based on Deep and Shallow Feature Fusion and Attention Mechanism. Remote Sens., 14.","DOI":"10.3390\/rs14051243"},{"key":"ref_10","first-page":"1","article-title":"Hyperspectral Image Stripe Detection and Correction Using Gabor Filters and Subspace Representation","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TIP.2022.3226406","article-title":"Tensor Cascaded-Rank Minimization in Subspace: A Unified Regime for Hyperspectral Image Low-Level Vision","volume":"32","author":"Sun","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105246","DOI":"10.1016\/j.jobe.2022.105246","article-title":"Vision-based concrete crack detection using a hybrid framework considering noise effect","volume":"61","author":"Yu","year":"2022","journal-title":"J. Build. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1177\/14613484211044627","article-title":"Magnetorheological Elastomer based torsional vibration isolator for application in a prototype drilling shaft","volume":"41","author":"Syam","year":"2022","journal-title":"J. Low Freq. Noise Vib. Act. Control"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1023\/B:JMIV.0000011321.19549.88","article-title":"An algorithm for total variation minimization and applications","volume":"20","author":"Chambolle","year":"2004","journal-title":"J. Math. Imag. Vis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1137\/040604297","article-title":"Aspects of total variation regularized li function approximation","volume":"65","author":"Chan","year":"2005","journal-title":"SIAM J. Appl. Math."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1137\/040605412","article-title":"An iterative regularization method for total variation-based image restoration","volume":"4","author":"Osher","year":"2005","journal-title":"Multiscale Model. Simul."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"511","DOI":"10.3934\/ipi.2011.5.511","article-title":"Non-local regularization of inverse problems","volume":"5","author":"Peyre","year":"2011","journal-title":"Inverse Probl. Imaging"},{"key":"ref_18","unstructured":"Condat, L. (2014, January 1\u20135). Semi-local total variation for regularization of inverse problems. Proceedings of the 2014 22nd European Signal Processing Conference (EUSIPCO), Lisbon, Portugal."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.compeleceng.2018.03.014","article-title":"Non-local total variation regularization models for image restoration","volume":"67","author":"Jidesh","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3736","DOI":"10.1109\/TIP.2006.881969","article-title":"Image denoising via sparse and redundant representations over learned dictionaries","volume":"15","author":"Elad","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/TIP.2007.911828","article-title":"Sparse representation for color image restoration","volume":"17","author":"Mairal","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","unstructured":"Mairal, J., Bach, F., Ponce, J., Sapiro, G., and Zisserman, A. (2021, January 10\u201317). Non-local sparse models for image restoration. Proceedings of the IEEE 12th International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.1109\/TIP.2012.2235847","article-title":"Nonlocally centralized sparse representation for image restoration","volume":"22","author":"Dong","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s11263-015-0808-y","article-title":"Image restoration via simultaneous sparse coding: Where structured sparsity meets Gaussian scale mixture","volume":"114","author":"Dong","year":"2015","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1049\/iet-ipr.2017.0518","article-title":"Iterative weighted sparse representation for X-ray cardiovascular angiogram image denoising over learned dictionary","volume":"12","author":"Huang","year":"2018","journal-title":"IET Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gu, S., Zhang, L., Zuo, W., and Feng, X. (2014, January 23\u201328). Weighted nuclear norm minimization with application to image denoising. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.366"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4842","DOI":"10.1109\/TIP.2016.2599290","article-title":"Weighted schatten p-norm minimization for image denoising and background subtraction","volume":"25","author":"Xie","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.sigpro.2016.07.031","article-title":"Structure tensor total variation-regularized weighted nuclear norm minimization for hyperspectral image mixed denoising","volume":"131","author":"Wu","year":"2017","journal-title":"Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1007\/s11760-017-1105-8","article-title":"Iterative weighted nuclear norm for X-ray cardiovascular angiogram image denoising","volume":"11","author":"Huang","year":"2017","journal-title":"Signal Image Video Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3171","DOI":"10.1109\/TIP.2017.2676466","article-title":"Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-Rank Approximation","volume":"26","author":"Huang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.jvcir.2017.11.019","article-title":"Principal component dictionary-based patch grouping for image denoising","volume":"50","author":"Yao","year":"2018","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1109\/TPAMI.2016.2596743","article-title":"Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration","volume":"39","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., and Zhang, L. (2017, January 21\u201326). Learning deep CNN denoiser prior for image restoration. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.300"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","article-title":"FFDNet: Toward a fast and flexible solution for CNN-based image denoising","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X., and Xu, C. (2017, January 22\u201329). MemNet: A persistent memory network for image restoration. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.486"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5944","DOI":"10.1109\/TIP.2021.3090531","article-title":"Deep K-SVD Denoising","volume":"30","author":"Scetbon","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, H., Yong, H., and Zhang, L. (2021, January 20\u201325). Deep convolutional dictionary learning for image denoising. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00069"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1109\/TGRS.2009.2033587","article-title":"Statistical linear destriping of satellite-based pushbroom-type images","volume":"48","author":"Carfantan","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1109\/TIP.2012.2206037","article-title":"Variational algorithms to remove stationary noise: Applications to microscopy imaging","volume":"21","author":"Fehrenbach","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_41","first-page":"3049","article-title":"Stripe noise separation and removal in remote sensing images by consideration of the global sparsity and local variational properties","volume":"54","author":"Liu","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_42","unstructured":"Liu, X., Lu, X., Shen, H., Yuan, Q., and Zhang, L. (2018). Oblique stripe removal in remote sensing images via oriented variation. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7018","DOI":"10.1109\/TGRS.2016.2594080","article-title":"Remote sensing image stripe noise removal: From image decomposition perspective","volume":"54","author":"Chang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chang, Y., Yan, L., and Zhong, S. (2017, January 22\u201329). Transformed low-rank model for line pattern noise removal. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.191"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6869","DOI":"10.1109\/TGRS.2020.3024623","article-title":"Hyperspectral image restoration: Where does the low-rank property exist","volume":"59","author":"Chang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, Y., Huang, T.-Z., Zhao, X.L., Deng, L.J., and Huang, J. (2017). Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint. Remote Sens., 9.","DOI":"10.3390\/rs9060559"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.neucom.2017.05.018","article-title":"Group sparsity based regularization model for remote sensing image stripe noise removal","volume":"267","author":"Chen","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1109\/LGRS.2018.2811468","article-title":"Destriping remote sensing image via low-rank approximation and nonlocal total variation","volume":"15","author":"Cao","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Song, Q., Wang, Y., Yan, X., and Gu, H. (2018). Remote sensing images stripe noise removal by double sparse regulation and region separation. Remote Sens., 10.","DOI":"10.3390\/rs10070998"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2724","DOI":"10.1166\/jctn.2018.7530","article-title":"Stripe noise separation and removal in remote sensing images","volume":"15","author":"Dhivya","year":"2018","journal-title":"J. Comput. Theor. Nanosci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2308","DOI":"10.1109\/TGRS.2019.2947599","article-title":"Multiscale intensity propagation to remove multiplicative stripe noise from remote sensing images","volume":"58","author":"Cui","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JPHOT.2017.2717948","article-title":"Single infrared image stripe noise removal using deep convolutional networks","volume":"9","author":"Kuang","year":"2017","journal-title":"IEEE Photonics J."},{"key":"ref_53","first-page":"1","article-title":"Removing stripe noise from infrared cloud images via deep convolutional networks","volume":"10","author":"Xiao","year":"2018","journal-title":"IEEE Photonics J."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"111416","DOI":"10.1016\/j.rse.2019.111416","article-title":"Satellite-ground intergraded destriping network: A new perspective for eo-1 hyperion and chinese hyperspectral satellite datasets","volume":"237","author":"Zhong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"6823","DOI":"10.1109\/TGRS.2020.3025601","article-title":"Unsupervised denoising for satellite imagery using wavelet directional cyclegan","volume":"59","author":"Song","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"6958","DOI":"10.1109\/TGRS.2020.2978276","article-title":"Joint analysis and weighted synthesis sparsity priors for simultaneous denoising and destriping optical remote sensing images","volume":"58","author":"Huang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1109\/LGRS.2013.2285124","article-title":"Simultaneous destriping and denoising for remote sensing images with unidirectional total variation and sparse representation","volume":"11","author":"Chang","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"10410","DOI":"10.1109\/TGRS.2019.2935150","article-title":"Hyperspectral restoration via L0 gradient regularized low-rank tensor factorization","volume":"57","author":"Xiong","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TGRS.2019.2940534","article-title":"Mixed noise removal in hyperspectral image via low-fibered-rank regularization","volume":"58","author":"Zheng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1109\/TGRS.2019.2948601","article-title":"Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints","volume":"58","author":"Liu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3309","DOI":"10.1109\/TGRS.2020.3007945","article-title":"Hyperspectral image restoration via global L1\u22122 spatial-spectral total variation regularized local low-rank tensor recovery","volume":"59","author":"Zeng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/TIP.2019.2926736","article-title":"Hyperspectral images denoising via nonconvex regularized low-rank and sparse matrix decomposition","volume":"29","author":"Xie","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1109\/TGRS.2020.2999634","article-title":"Hyperspectral image restoration using adaptive anisotropy total variation and nuclear norms","volume":"59","author":"Hu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"7695","DOI":"10.1109\/TGRS.2021.3055516","article-title":"l0 \u2212 l1 Hybrid total variation regularization and its applications on hyperspectral image mixed noise removal and compressed sensing","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","first-page":"5535169","article-title":"Hyperspectral image denoising via nonconvex logarithmic penalty","volume":"2021","author":"Wang","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_66","unstructured":"Dong, W., Zuo, W., Zhang, D., Zhang, L., and Yang, M.H. (2019). Simultaneous fidelity and regularization learning for image restoration. arXiv."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1109\/TPAMI.2018.2873610","article-title":"Denoising prior driven deep neural network for image restoration","volume":"41","author":"Dong","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"6360","DOI":"10.1109\/TPAMI.2021.3088914","article-title":"Plug-and-play image restoration with deep denoiser prior","volume":"44","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5737","DOI":"10.1080\/01431161.2019.1580821","article-title":"Unidirectional variation and deep CNN denoiser priors for simultaneously destriping and denoising optical remote sensing images","volume":"40","author":"Huang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"103004","DOI":"10.1016\/j.cviu.2020.103004","article-title":"Hyperspectral image restoration via CNN denoiser prior regularized low-rank tensor recovery","volume":"197\u2013198","author":"Zeng","year":"2020","journal-title":"Comput. Vis. Image Unders."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"D155","DOI":"10.1364\/AO.57.00D155","article-title":"Single-image-based non-uniformity correction of uncooled long-wave infrared detectors: A deep-learning approach","volume":"57","author":"He","year":"2018","journal-title":"Appl. Opt."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1109\/TGRS.2018.2859203","article-title":"HSI-DeNet: Hyperspectral image restoration via convolutional neural network","volume":"57","author":"Chang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"7317","DOI":"10.1109\/TGRS.2019.2912909","article-title":"Hybrid noise removal in hyperspectral imagery with a spatial-spectral gradient network","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geos. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhao, X., Jiang, T., Zheng, Y., and Chang, Y. (2021). Unsupervised hyperspectral mixed noise removal via spatial-spectral constrained deep image prior. arXiv.","DOI":"10.1109\/JSTARS.2021.3111404"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"2449","DOI":"10.1109\/TMM.2021.3081873","article-title":"MFDNet: Collaborative poses perception and matrix fisher distribution for head pose estimation","volume":"24","author":"Liu","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_77","first-page":"903","article-title":"SAU-Net: Efficient 3D spine MRI segmentation using inter-slice attention","volume":"121","author":"Zhang","year":"2020","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Yong, H., Huang, J., Meng, D., Hua, X., and Zhang, L. (2020, January 23\u201328). Momentum batch normalization for deep learning with small batch size. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58610-2_14"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., and Lenc, K. (2015, January 26\u201330). MatConvNet: Convolutional neural networks for matlab. Proceedings of the 23rd ACM Conference on Multimedia Conference, Brisbane, Australia.","DOI":"10.1145\/2733373.2807412"},{"key":"ref_81","unstructured":"(2018, January 30). MODIS Data, Available online: https:\/\/modis.gsfc.nasa.gov\/data\/."},{"key":"ref_82","unstructured":"(2018, January 30). A Freeware Multispectral Image Data Analysis System. Available online: https:\/\/engineering.purdue.edu\/~biehl\/MultiSpec\/hyperspectral.html."},{"key":"ref_83","unstructured":"(2018, January 30). Gloabal Digital Product Sample. Available online: http:\/\/www.digitalglobe.com\/product-samples."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Tsuruoka, Y., Tsujii, J., and Ananiadou, S. (2009, January 2\u20137). Stochastic gradient descent training for L1-regularized log-linear models with cumulative penalty. Proceedings of the ACL 2009 the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Singapore.","DOI":"10.3115\/1687878.1687946"},{"key":"ref_85","unstructured":"Kingma, D.P., and Ba, J. (2015). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1080\/01431160310001618770","article-title":"Noise over water surfaces in Landsat TM images","volume":"25","author":"Nichol","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u2018completely blind\u2019 image quality analyzer","volume":"20","author":"Mittal","year":"2013","journal-title":"IEEE Signal Process. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/443\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:03:29Z","timestamp":1760119409000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/2\/443"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,11]]},"references-count":87,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15020443"],"URL":"https:\/\/doi.org\/10.3390\/rs15020443","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,11]]}}}