{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:26:43Z","timestamp":1781368003991,"version":"3.54.1"},"reference-count":33,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T00:00:00Z","timestamp":1635984000000},"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":["2019YFC1510503"],"award-info":[{"award-number":["2019YFC1510503"]}],"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":["61801384"],"award-info":[{"award-number":["61801384"]}],"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":["41601353"],"award-info":[{"award-number":["41601353"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Shaanxi Province of China","award":["2020JM-415"],"award-info":[{"award-number":["2020JM-415"]}]},{"name":"Key Research and Development Program of Shaanxi Province of China","award":["2020KW-010"],"award-info":[{"award-number":["2020KW-010"]}]},{"name":"Key Research and Development Program of Shaanxi Province of China","award":["2021KW-05"],"award-info":[{"award-number":["2021KW-05"]}]},{"name":"Northwest University Paleontological Bioinformatics Innovation Team","award":["2019TD-012"],"award-info":[{"award-number":["2019TD-012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The haze in remote sensing images can cause the decline of image quality and bring many obstacles to the applications of remote sensing images. Considering the non-uniform distribution of haze in remote sensing images, we propose a single remote sensing image dehazing method based on the encoder\u2013decoder architecture, which combines both wavelet transform and deep learning technology. To address the clarity issue of remote sensing images with non-uniform haze, we preliminary process the input image by the dehazing method based on the atmospheric scattering model, and extract the first-order low-frequency sub-band information of its 2D stationary wavelet transform as an additional channel. Meanwhile, we establish a large-scale hazy remote sensing image dataset to train and test the proposed method. Extensive experiments show that the proposed method obtains greater advantages over typical traditional methods and deep learning methods qualitatively. For the quantitative aspects, we take the average of four typical deep learning methods with superior performance as a comparison object using 500 random test images, and the peak-signal-to-noise ratio (PSNR) value using the proposed method is improved by 3.5029 dB, and the structural similarity (SSIM) value is improved by 0.0295, respectively. Based on the above, the effectiveness of the proposed method for the problem of remote sensing non-uniform dehazing is verified comprehensively.<\/jats:p>","DOI":"10.3390\/rs13214443","type":"journal-article","created":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T22:25:54Z","timestamp":1636064754000},"page":"4443","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4066-1802","authenticated-orcid":false,"given":"Bo","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanting","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinshuai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1026-0060","authenticated-orcid":false,"given":"Lin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxuan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wierzbicki, D., Kedzierski, M., and Grochala, A. (2019). A Method for Dehazing Images Obtained from Low Altitudes during High-Pressure Fronts. Remote Sens., 12.","DOI":"10.3390\/rs12010025"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Luo, Y., Wei, H., Li, Y., Qi, G., Mazur, N., Li, Y., and Li, P. (2021). Atmospheric Light Estimation Based Remote Sensing Image Dehazing. Remote Sens., 13.","DOI":"10.3390\/rs13132432"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1063\/1.3037551","article-title":"Optics of the Atmosphere: Scattering by Molecules and Particles","volume":"30","author":"Mccartney","year":"1977","journal-title":"Phys. Today"},{"key":"ref_4","unstructured":"Narasimhan, S.G., and Nayar, S.K. (2000, January 15). Chromatic Framework for Vision in Bad Weather. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition\u2014CVPR 2000, Hilton Head, SC, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1023\/A:1016328200723","article-title":"Vision and the atmosphere","volume":"48","author":"Narasimhan","year":"2002","journal-title":"Int. J. Comput. Vis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1109\/TPAMI.2010.168","article-title":"Single Image Haze Removal Using Dark Channel Prior","volume":"33","author":"He","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1360612.1360671","article-title":"Single Image Dehazing","volume":"27","author":"Fattal","year":"2008","journal-title":"ACM Trans. Graph."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Berman, D., Treibitz, T., and Avidan, S. (2016, January 27\u201330). Non-local Image Dehazing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition\u2014CVPR 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.185"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3522","DOI":"10.1109\/TIP.2015.2446191","article-title":"A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior","volume":"24","author":"Zhu","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1016\/j.future.2021.06.045","article-title":"Human Action Recognition Using Attention Based LSTM Network with Dilated CNN Features","volume":"125","author":"Muhammad","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5116","DOI":"10.1002\/int.22505","article-title":"Optimal Feature Selection Based Speech Emotion Recognition Using Two-Stream Deep Convolutional Neural Network","volume":"36","author":"Mustaqeem","year":"2021","journal-title":"Int. J. Intell. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mei, K., Jiang, A., Li, J., and Wang, M. (2018, January 2\u20136). Progressive Feature Fusion Network for Realistic Image Dehazing. Proceedings of the Asian Conference on Computer Vision\u2014ACCV 2018, Perth, Australia.","DOI":"10.1007\/978-3-030-20887-5_13"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, H.H., and Fu, Y. (2019, January 22\u201325). Wavelet U-Net and the Chromatic Adaptation Transform for Single Image Dehazing. Proceedings of the IEEE International Conference on Image Processing\u2014ICIP 2019, Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803391"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Qu, Y., Chen, Y., Huang, J., and Xie, Y. (2019, January 15\u201320). Enhanced Pix2pix Dehazing Network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition\u2014CVPR 2019, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00835"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1109\/LGRS.2020.3006533","article-title":"A Coarse-to-Fine Two-stage Attentive Network for Haze Removal of Remote Sensing Images","volume":"18","author":"Li","year":"2020","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition\u2014CVPR 2015, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_17","unstructured":"Yu, F., and Koltun, V. (2016, January 2\u20134). Multi-scale Context Aggregation by Dilated Convolutions. Proceedings of the International Conference on Learning Representations\u2014ICLR 2016, San Juan, PR, USA."},{"key":"ref_18","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\u2014CVPR 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_19","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2016). Instance Normalization: The Missing Ingredient for Fast Stylization. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, D., He, M., Fan, Q., Liao, J., Zhang, L., Hou, D., Yuan, L., and Hua, G. (2019, January 7\u201311). Gated Context Aggregation Network for Image Dehazing and Deraining. Proceedings of the IEEE Winter Conference on Applications of Computer Vision\u2014WACV 2019, Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2019.00151"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Ji, S. (2018, January 19\u201323). Smoothed Dilated Convolutions for Improved Dense Prediction. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining\u2014KDD 2018, London, UK.","DOI":"10.1145\/3219819.3219944"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Deng, X., Yang, R., Xu, M., and Dragotti, P.L. (November, January 27). Wavelet Domain Style Transfer for an Effective Perception-Distortion Tradeoff in Single Image Super-Resolution. Proceedings of the IEEE\/CVF International Conference on Computer Vision\u2014ICCV 2019, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00317"},{"key":"ref_23","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":"Zhou","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","unstructured":"Zhou, W., Simoncelli, E.P., and Bovik, A.C. (2003, January 9\u201312). Multiscale Structural Similarity for Image Quality Assessment. Proceedings of the Thrity-Seventh Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","article-title":"Loss Functions for Image Restoration with Neural Networks","volume":"3","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided image filtering","volume":"35","author":"He","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1109\/TIP.2019.2934360","article-title":"RYF-Net: Deep Fusion Network for Single Image Haze Removal","volume":"29","author":"Dudhane","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","article-title":"Deep Learning Based Feature Selection for Remote Sensing Scene Classification","volume":"12","author":"Qin","year":"2015","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_30","unstructured":"(2021, August 30). BH-Pools\/Watertanks Datasets. Available online: http:\/\/patreo.dcc.ufmg.br\/2020\/07\/29\/bh-pools-watertanks-datasets\/."},{"key":"ref_31","unstructured":"(2021, August 30). Geospatial Data Cloud. Available online: http:\/\/www.gscloud.cn."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Berman, D., Treibitz, T., and Avidan, S. (2017, January 12\u201314). Air-light Estimation Using Haze-lines. Proceedings of the IEEE International Conference on Computational Photography\u2014ICCP 2017, Stanford, CA, USA.","DOI":"10.1109\/ICCPHOT.2017.7951489"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.1109\/TIP.2011.2109730","article-title":"FSIM: A Feature Similarity Index for Image Quality Assessment","volume":"20","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4443\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:25:55Z","timestamp":1760167555000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4443"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,4]]},"references-count":33,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214443"],"URL":"https:\/\/doi.org\/10.3390\/rs13214443","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,4]]}}}