{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T21:41:08Z","timestamp":1771623668815,"version":"3.50.1"},"reference-count":78,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFC1510905"],"award-info":[{"award-number":["2019YFC1510905"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["61871011"],"award-info":[{"award-number":["61871011"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019YFC1510905"],"award-info":[{"award-number":["2019YFC1510905"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871011"],"award-info":[{"award-number":["61871011"]}],"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>Cloud occlusion phenomena are widespread in optical remote sensing (RS) images, leading to information loss and image degradation and causing difficulties in subsequent applications such as land surface classification, object detection, and land change monitoring. Therefore, thin cloud removal is a key preprocessing procedure for optical RS images, and has great practical value. Recent deep learning-based thin cloud removal methods have achieved excellent results. However, these methods have a common problem in that they cannot obtain large receptive fields while preserving image detail. In this paper, we propose a novel wavelet-integrated convolutional neural network for thin cloud removal (WaveCNN-CR) in RS images that can obtain larger receptive fields without any information loss. WaveCNN-CR generates cloud-free images in an end-to-end manner based on an encoder\u2013decoder-like architecture. In the encoding stage, WaveCNN-CR first extracts multi-scale and multi-frequency components via wavelet transform, then further performs feature extraction for each high-frequency component at different scales by multiple enhanced feature extraction modules (EFEM) separately. In the decoding stage, WaveCNN-CR recursively concatenates the processed low-frequency and high-frequency components at each scale, feeds them into EFEMs for feature extraction, then reconstructs the high-resolution low-frequency component by inverse wavelet transform. In addition, the designed EFEM consisting of an attentive residual block (ARB) and gated residual block (GRB) is used to emphasize the more informative features. ARB and GRB enhance features from the perspective of global and local context, respectively. Extensive experiments on the T-CLOUD, RICE1, and WHUS2-CR datasets demonstrate that our WaveCNN-CR significantly outperforms existing state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs15030781","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T10:19:28Z","timestamp":1675073968000},"page":"781","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Wavelet Integrated Convolutional Neural Network for Thin Cloud Removal in Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Yue","family":"Zi","sequence":"first","affiliation":[{"name":"Department of Aerospace Information Engineering, School of Astronautics, Beihang University, Beijing 100191, China"}]},{"given":"Haidong","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Aerospace Information Engineering, School of Astronautics, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5681-2345","authenticated-orcid":false,"given":"Fengying","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Aerospace Information Engineering, School of Astronautics, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8786-2540","authenticated-orcid":false,"given":"Zhiguo","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Aerospace Information Engineering, School of Astronautics, Beihang University, Beijing 100191, China"}]},{"given":"Xuedong","family":"Song","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Control Technology Institute, Shanghai Academy of Spaceflight Technology, Shanghai 201109, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1109\/LGRS.2018.2880756","article-title":"CoinNet: Copy initialization network for multispectral imagery semantic segmentation","volume":"16","author":"Pan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1109\/LGRS.2020.3010591","article-title":"An end-to-end network for remote sensing imagery semantic segmentation via joint pixel-and representation-level domain adaptation","volume":"18","author":"Shi","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1109\/LGRS.2019.2954199","article-title":"Finding arbitrary-oriented ships from remote sensing images using corner detection","volume":"17","author":"Chen","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7933","DOI":"10.1109\/TGRS.2020.3048384","article-title":"DCL-Net: Augmenting the capability of classification and localization for remote sensing object detection","volume":"59","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, H., and Shi, Z. (2020). A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens., 12.","DOI":"10.3390\/rs12101662"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Rossow, W.B., Lacis, A.A., Oinas, V., and Mishchenko, M.I. (2004). Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data. J. Geophys. Res. Atmos., 109.","DOI":"10.1029\/2003JD004457"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3826","DOI":"10.1109\/TGRS.2012.2227333","article-title":"Spatial and temporal distribution of clouds observed by MODIS onboard the Terra and Aqua satellites","volume":"51","author":"King","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.isprsjprs.2014.06.011","article-title":"An effective thin cloud removal procedure for visible remote sensing images","volume":"96","author":"Shen","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1806","DOI":"10.1109\/LSP.2015.2432466","article-title":"Haze removal for a single remote sensing image based on deformed haze imaging model","volume":"22","author":"Pan","year":"2015","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1109\/LGRS.2018.2874084","article-title":"Haze and thin cloud removal via sphere model improved dark channel prior","volume":"16","author":"Li","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5895","DOI":"10.1109\/TGRS.2013.2293662","article-title":"Haze detection and removal in remotely sensed multispectral imagery","volume":"52","author":"Makarau","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","first-page":"379","article-title":"Combined haze and cirrus removal for multispectral imagery","volume":"13","author":"Makarau","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","first-page":"503","article-title":"Thin cloud removal method in color remote sensing image","volume":"43","author":"He","year":"2017","journal-title":"Opt. Tech."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"095053","DOI":"10.1117\/1.JRS.9.095053","article-title":"Thin cloud removal from remote sensing images using multidirectional dual tree complex wavelet transform and transfer least square support vector regression","volume":"9","author":"Hu","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11481","DOI":"10.3390\/rs70911481","article-title":"Removal of thin clouds in Landsat-8 OLI data with independent component analysis","volume":"7","author":"Shen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lv, H., Wang, Y., and Gao, Y. (2018, January 22\u201327). Using independent component analysis and estimated thin-cloud reflectance to remove cloud effect on Landsat-8 oli band data. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518318"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.isprsjprs.2019.01.025","article-title":"Thin cloud removal from optical remote sensing images using the noise-adjusted principal components transform","volume":"149","author":"Xu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1080\/01431161.2017.1407048","article-title":"Haze removal for new generation optical sensors","volume":"39","author":"Hong","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2016.03.034","article-title":"An empirical and radiative transfer model based algorithm to remove thin clouds in visible bands","volume":"179","author":"Lv","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/TGRS.2018.2861939","article-title":"Modeling of thin-cloud TOA reflectance using empirical relationships and two Landsat-8 visible band data","volume":"57","author":"Lv","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","unstructured":"Xu, M., Jia, X., and Pickering, M. (2014, January 13\u201318). Automatic cloud removal for Landsat 8 OLI images using cirrus band. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhou, B., and Wang, Y. (August, January 28). A thin-cloud removal approach combining the cirrus band and RTM-based algorithm for Landsat-8 OLI data. Proceedings of the 2019 IEEE Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898644"},{"key":"ref_25","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"115406","DOI":"10.1016\/j.engstruct.2022.115406","article-title":"Automatic classification of asphalt pavement cracks using a novel integrated generative adversarial networks and improved VGG model","volume":"277","author":"Que","year":"2023","journal-title":"Eng. Struct."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107079","DOI":"10.1016\/j.compag.2022.107079","article-title":"Rachis detection and three-dimensional localization of cut off point for vision-based banana robot","volume":"198","author":"Wu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2019.05.003","article-title":"Thin cloud removal with residual symmetrical concatenation network","volume":"153","author":"Li","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6507605","DOI":"10.1109\/LGRS.2022.3161062","article-title":"An effective network integrating residual learning and channel attention mechanism for thin cloud removal","volume":"19","author":"Wen","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, J., Wu, Z., Hu, Z., Li, Z., Wang, Y., and Molinier, M. (2021). Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery. Remote Sens., 13.","DOI":"10.3390\/rs13010157"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1049\/ipr2.12224","article-title":"MSAR-DefogNet: Lightweight cloud removal network for high resolution remote sensing images based on multi scale convolution","volume":"16","author":"Zhou","year":"2022","journal-title":"IET Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ding, H., Zi, Y., and Xie, F. (2022, January 4\u20138). Uncertainty-based thin cloud removal network via conditional variational autoencoders. Proceedings of the 2022 Asian Conference on Computer Vision (ACCV), Macau SAR, China.","DOI":"10.1007\/978-3-031-26313-2_4"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"6371","DOI":"10.1109\/TGRS.2020.3027819","article-title":"Single image cloud removal using U-Net and generative adversarial networks","volume":"59","author":"Zheng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","first-page":"1","article-title":"Cloudy image arithmetic: A cloudy scene synthesis paradigm with an application to deep-learning-based thin cloud removal","volume":"60","author":"Xu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Enomoto, K., Sakurada, K., Wang, W., Fukui, H., Matsuoka, M., Nakamura, R., and Kawaguchi, N. (2017, January 21\u201326). Filmy cloud removal on satellite imagery with multispectral conditional generative adversarial nets. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.197"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, R., Xie, F., and Chen, J. (2018, January 11\u201314). Single image thin cloud removal for remote sensing images based on conditional generative adversarial nets. Proceedings of the Tenth International Conference on Digital Image Processing (ICDIP), Shanghai, China.","DOI":"10.1117\/12.2503148"},{"key":"ref_42","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wen, X., Pan, Z., Hu, Y., and Liu, J. (2021). Generative adversarial learning in YUV color space for thin cloud removal on satellite imagery. Remote Sens., 13.","DOI":"10.3390\/rs13061079"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhang, X., Yu, Q., and Ma, C. (2022, January 17\u201322). An improved method for removal of thin clouds in remote sensing images by generative adversarial network. Proceedings of the 2022 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884704"},{"key":"ref_45","unstructured":"Pan, H. (2020). Cloud removal for remote sensing imagery via spatial attention generative adversarial network. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1049\/ipr2.12067","article-title":"Attentive generative adversarial network for removing thin cloud from a single remote sensing image","volume":"15","author":"Chen","year":"2021","journal-title":"IET Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"112902","DOI":"10.1016\/j.rse.2022.112902","article-title":"Attention mechanism-based generative adversarial networks for cloud removal in Landsat images","volume":"271","author":"Xu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Xu, Z., Wu, K., and Ren, P. (2022, January 17\u201322). Recovering thin cloud covered regions in GF satellite images based on cloudy image arithmetic+. Proceedings of the 2022 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884528"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3811","DOI":"10.1109\/JSTARS.2021.3068166","article-title":"Thin cloud removal for multispectral remote sensing images using convolutional neural networks combined with an imaging model","volume":"14","author":"Zi","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yu, W., Zhang, X., Pun, M.O., and Liu, M. (2021, January 11\u201316). A hybrid model-based and data-driven approach for cloud removal in satellite imagery using multi-scale distortion-aware networks. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554963"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5512605","DOI":"10.1109\/LGRS.2022.3144686","article-title":"Cloud removal in optical remote sensing imagery using multiscale distortion-aware networks","volume":"19","author":"Yu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.isprsjprs.2020.06.021","article-title":"Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion","volume":"166","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2021.3140033","article-title":"Thin cloud removal for remote sensing images using a physical-model-based CycleGAN with unpaired data","volume":"19","author":"Zi","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal decomposition: The wavelet representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Yu, H., Zheng, N., Zhou, M., Huang, J., Xiao, Z., and Zhao, F. (2022, January 23\u201327). Frequency and spatial dual guidance for image dehazing. Proceedings of the 2022 European Conference on Computer Vision (ECCV), Tel-Aviv, Israel.","DOI":"10.1007\/978-3-031-19800-7_11"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Li, Q., Shen, L., Guo, S., and Lai, Z. (2020, January 13\u201319). Wavelet integrated CNNs for noise-robust image classification. Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00727"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Huang, H., He, R., Sun, Z., and Tan, T. (2017, January 22\u201329). Wavelet-SRNet: A wavelet-based CNN for multi-scale face super resolution. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.187"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Liu, P., Zhang, H., Zhang, K., Lin, L., and Zuo, W. (2018, January 18\u201322). Multi-level wavelet-CNN for image restoration. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00121"},{"key":"ref_59","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":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"44544","DOI":"10.1109\/ACCESS.2019.2908720","article-title":"Wavelet deep neural network for stripe noise removal","volume":"7","author":"Guan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, W.T., Fang, H.Y., Hsieh, C.L., Tsai, C.C., Chen, I.H., Ding, J.J., and Kuo, S.Y. (2021, January 10\u201317). ALL snow removed: Single image desnowing algorithm using hierarchical dual-tree complex wavelet representation and contradict channel loss. Proceedings of the 2021 IEEE International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00416"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Yang, M., Wang, Z., Chi, Z., and Feng, W. (2022, January 23\u201327). WaveGAN: Frequency-aware GAN for high-fidelity few-shot image generation. Proceedings of the 2022 European Conference on Computer Vision (ECCV), Tel-Aviv, Israel.","DOI":"10.1007\/978-3-031-19784-0_1"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/BF01456927","article-title":"Zur theorie der orthogonalen funktionensysteme","volume":"71","author":"Haar","year":"1911","journal-title":"Math. Ann."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the 2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional block attention module. Proceedings of the 2018 European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_67","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_68","unstructured":"Ba, J.L., Kiros, J.R., and Hinton, G.E. (2016). Layer normalization. arXiv."},{"key":"ref_69","unstructured":"Hendrycks, D., and Gimpel, K. (2016). Gaussian error linear units (gelus). arXiv."},{"key":"ref_70","unstructured":"Gondal, M.W., Scholkopf, B., and Hirsch, M. (2018, January 8\u201314). The unreasonable effectiveness of texture transfer for single image super-resolution. Proceedings of the 2018 European Conference on Computer Vision (ECCV), Munich, Germany."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 21\u201326). Enhanced deep residual networks for single image super-resolution. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_72","unstructured":"Lin, D., Xu, G., Wang, X., Wang, Y., Sun, X., and Fu, K. (2019). A remote sensing image dataset for cloud removal. arXiv."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1049\/el:20080522","article-title":"Scope of validity of PSNR in image\/video quality assessment","volume":"44","author":"Ghanbari","year":"2008","journal-title":"Electron. Lett."},{"key":"ref_74","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_75","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1002\/col.1049","article-title":"The development of the CIE 2000 colour-difference formula: CIEDE2000","volume":"26","author":"Luo","year":"2001","journal-title":"Color Res. Appl."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1002\/col.20070","article-title":"The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations","volume":"30","author":"Sharma","year":"2005","journal-title":"Color Res. Appl."},{"key":"ref_77","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., and Li, M. (2019, January 15\u201320). Bag of tricks for image classification with convolutional neural networks. Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00065"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/781\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:19:29Z","timestamp":1760120369000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/781"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,30]]},"references-count":78,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15030781"],"URL":"https:\/\/doi.org\/10.3390\/rs15030781","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,30]]}}}