{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T15:40:11Z","timestamp":1784389211533,"version":"3.55.0"},"reference-count":58,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,3]],"date-time":"2017-06-03T00:00:00Z","timestamp":1496448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing images have been used in many fields, such as urban planning, military, and environment monitoring, but corruption by stripe noise limits its subsequent applications. Most existing stripe noise removal (destriping) methods aim to directly estimate the clear images from the stripe images without considering the intrinsic properties of stripe noise, which causes the image structure destroyed. In this paper, we propose a new destriping method from the perspective of image decomposition, which takes the intrinsic properties of stripe noise and image characteristics into full consideration. The proposed method integrates the unidirectional total variation (TV) regularization, group sparsity regularization, and TV regularization together in an image decomposition framework. The first two terms are utilized to exploit the stripe noise properties by implementing statistical analysis, and the TV regularization is adopted to explore the spatial piecewise smooth structure of stripe-free image. Moreover, an efficient alternating minimization scheme is designed to solve the proposed model. Extensive experiments on simulated and real data demonstrate that our method outperforms several existing state-of-the-art destriping methods in terms of both quantitative and qualitative assessments.<\/jats:p>","DOI":"10.3390\/rs9060559","type":"journal-article","created":{"date-parts":[[2017,6,6]],"date-time":"2017-06-06T10:53:09Z","timestamp":1496746389000},"page":"559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint"],"prefix":"10.3390","volume":"9","author":[{"given":"Yong","family":"Chen","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences\/Resrarch Center for Image and Vision Computing, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7766-230X","authenticated-orcid":false,"given":"Ting-Zhu","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences\/Resrarch Center for Image and Vision Computing, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi-Le","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences\/Resrarch Center for Image and Vision Computing, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang-Jian","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences\/Resrarch Center for Image and Vision Computing, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences\/Resrarch Center for Image and Vision Computing, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1109\/TGRS.2003.817206","article-title":"Destriping CMODIS data by power filtering","volume":"41","author":"Chen","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/TGRS.2015.2452812","article-title":"Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration","volume":"54","author":"He","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1109\/TGRS.2013.2284280","article-title":"Hyperspectral image restoration using low-rank matrix recovery","volume":"52","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3660","DOI":"10.1109\/TGRS.2012.2185054","article-title":"Hyperspectral Image denoising employing a spectral-spatial adaptive total variation model","volume":"50","author":"Yuan","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","first-page":"442","article-title":"Hyperspectral Image denoising using spatio-spectral total variation","volume":"13","author":"Aggarwal","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xu, Y., and Qian, Y. (2016). Group sparse nonnegative matrix factorization for hyperspectral image denoising. IGARSS, 6958\u20136961.","DOI":"10.1109\/IGARSS.2016.7730815"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2056","DOI":"10.1109\/JSTARS.2013.2264720","article-title":"A nonlocal weighted joint sparse representation classification method for hyperspectral imagery","volume":"7","author":"Zhang","year":"2014","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4484","DOI":"10.1109\/TGRS.2012.2191590","article-title":"Total variation spatial regularization for Sparse hyperspectral unmixing","volume":"50","author":"Iordache","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4045","DOI":"10.1109\/TGRS.2012.2227764","article-title":"Deblurring and sparse unmixing for hyperspectral images","volume":"51","author":"Zhao","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1109\/TGRS.2013.2240001","article-title":"Collaborative sparse regression for hyperspectral unmixing","volume":"52","author":"Iordache","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1016\/j.patcog.2010.01.016","article-title":"Segmentation and classification of hyperspectral images using watershed transformation","volume":"43","author":"Tarabalka","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/79.974730","article-title":"Anomaly detection from hyperspectral imagery","volume":"19","author":"Stein","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_13","first-page":"1417","article-title":"Destriping of Landsat MSS images by filtering techniques","volume":"58","author":"Chen","year":"1992","journal-title":"Photogramm. Eng. Remote Sensing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1117\/1.1383996","article-title":"Wavelet analysis for the elimination of striping noise in satellite images","volume":"40","author":"Torres","year":"2001","journal-title":"Opt. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1080\/01431160500185516","article-title":"Oblique striping removal in remote sensing imagery based on wavelet transform","volume":"27","author":"Chen","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8567","DOI":"10.1364\/OE.17.008567","article-title":"Stripe and ring artifact removal with combined wavelet-Fourier filtering","volume":"17","author":"Trtik","year":"2009","journal-title":"Opt. Express"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1016\/j.isprsjprs.2011.04.003","article-title":"De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering","volume":"66","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"68","DOI":"10.5589\/m07-067","article-title":"Automatic destriping of Hyperion imagery based on spectral moment matching","volume":"34","author":"Sun","year":"2008","journal-title":"Can. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1109\/TGRS.2007.895841","article-title":"Stripe noise reduction in MODIS data by combining histogram matching with facet filter","volume":"45","author":"Rakwatin","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/0146-664X(79)90035-2","article-title":"Destriping Landsat MSS images by histogram modification","volume":"10","author":"Horn","year":"1979","journal-title":"Comput. Gr. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/01431169008955060","article-title":"Destriping multiple sensor imagery by improved histogram matching","volume":"11","author":"Wegener","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2505","DOI":"10.1080\/01431160050030592","article-title":"Destriping multisensor imagery with moment matching","volume":"21","author":"Gadallah","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1109\/TGRS.2008.2005780","article-title":"A MAP-based algorithm for destriping and inpainting of remotely sensed images","volume":"47","author":"Shen","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2924","DOI":"10.1109\/TGRS.2011.2119399","article-title":"Toward optimal destriping of MODIS data using a unidirectional variational model","volume":"49","author":"Bouali","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"23307","DOI":"10.1364\/OE.21.023307","article-title":"Robust destriping method with unidirectional total variation and framelet regularization","volume":"21","author":"Chang","year":"2013","journal-title":"Opt. Express"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.ijleo.2015.09.246","article-title":"A destriping algorithm based on TV-Stokes and unidirectional total variation model","volume":"127","author":"Zhang","year":"2016","journal-title":"Optik-Int. J. Light Electron Opt."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1016\/j.ijleo.2015.02.045","article-title":"Robust destriping of MODIS and hyperspectral data using a hybrid unidirectional total variation model","volume":"126","author":"Zhou","year":"2015","journal-title":"Optik-Int. J. Light Electron Opt."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.infrared.2015.12.004","article-title":"Unidirectional total variation destriping using difference curvature in MODIS emissive bands","volume":"75","author":"Wang","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1109\/TGRS.2010.2081370","article-title":"Subspace-based striping noise reduction in hyperspectral images","volume":"49","author":"Acito","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4009","DOI":"10.1109\/TGRS.2012.2226730","article-title":"Graph-regularized low-rank representation for destriping of hyperspectral images","volume":"51","author":"Lu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1852","DOI":"10.1109\/TIP.2015.2404782","article-title":"Anisotropic spectral-spatial total variation model for multispectral remote sensing image destriping","volume":"24","author":"Chang","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3049","DOI":"10.1109\/TGRS.2015.2510418","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. Geosci. Remote Sens."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.ins.2014.10.041","article-title":"Image restoration using total variation with overlapping group sparsity","volume":"295","author":"Liu","year":"2015","journal-title":"Information Sciences"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2015","DOI":"10.1016\/j.camwa.2014.04.008","article-title":"High-order TVL1-based images restoration and spatially adapted regularization parameter selection","volume":"67","author":"Liu","year":"2014","journal-title":"Comput. Math. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.ins.2014.02.089","article-title":"Two soft-thresholding based iterative algorithms for image deblurring","volume":"271","author":"Huang","year":"2014","journal-title":"Information Sciences"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"717","DOI":"10.3934\/ipi.2013.7.717","article-title":"Nonstationary iterated thresholding algorithms for image deblurring","volume":"7","author":"Huang","year":"2013","journal-title":"Inverse Probl. Imaging"},{"key":"ref_40","unstructured":"Tikhonov, A., and Arsenin, V. (1977). Solutions of Ill-Posed Problems, Winston and Sons."},{"key":"ref_41","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":"Phy. D: Nonlinear Phenom."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1137\/13092472X","article-title":"A new convex optimization model for multiplicative noise and blur removal","volume":"7","author":"Zhao","year":"2014","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.cam.2015.03.035","article-title":"A fast image recovery algorithm based on splitting deblurring and denoising","volume":"287","author":"Deng","year":"2015","journal-title":"J. Comput. Appl. Math."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.ins.2015.07.049","article-title":"Tensor completion using total variation and low-rank matrix factorization","volume":"326","author":"Ji","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1080\/10556788.2014.955100","article-title":"An alternating direction method for total variation denoising","volume":"30","author":"Qin","year":"2015","journal-title":"Optim. Methods Softw."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Deng, W., Yin, W., and Zhang, Y. (2013). Group sparse optimization by alternating direction method. Proc. SPIE.","DOI":"10.1117\/12.2024410"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/BF01581204","article-title":"On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators","volume":"55","author":"Eckstein","year":"1992","journal-title":"Math. Program."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000016","article-title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","volume":"3","author":"Boyd","year":"2011","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1109\/18.382009","article-title":"De-noising by soft-thresholding","volume":"41","author":"Donoho","year":"1995","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1137\/S1064827598341384","article-title":"A Fast Algorithm for deblurring models with neumann boundary conditions","volume":"21","author":"Ng","year":"1999","journal-title":"SIAM J. Sci. Comput."},{"key":"ref_51","unstructured":"Liu, G., Lin, Z., and Yu, Y. (2010, January 21\u201324). Robust subspace segmentation by low-rank representation. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1023\/A:1017501703105","article-title":"Convergence of a block coordinate descent method for nondifferentiable minimization","volume":"109","author":"Tseng","year":"2001","journal-title":"J. Optim. Theory Appl."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1109\/TCSVT.2015.2475895","article-title":"Single-image super-resolution via an iterative reproducing kernel hilbert space method","volume":"26","author":"Deng","year":"2016","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_54","unstructured":"(2017, April 07). A Freeware Multispectral Image Data Analysis System. Available online: https:\/\/engineering.purdue.edu\/~biehl\/MultiSpec\/hyperspectral.html."},{"key":"ref_55","unstructured":"(2017, April 07). LAADS DAAC, Available online: https:\/\/ladsweb.nascom.nasa.gov."},{"key":"ref_56","unstructured":"(2017, April 07). Open Remote Sensing. Available online: https:\/\/openremotesensing.net."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_58","unstructured":"(2017, April 07). Index of Hyperspectral Imagedata, Available online: http:\/\/compression.jpl.nasa.gov\/hyperspectral\/imagedata."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/6\/559\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:37:54Z","timestamp":1760207874000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/6\/559"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,3]]},"references-count":58,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2017,6]]}},"alternative-id":["rs9060559"],"URL":"https:\/\/doi.org\/10.3390\/rs9060559","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,6,3]]}}}