{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T06:06:06Z","timestamp":1770271566701,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"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>JPEG-LS (a lossless (LS) compression standard developed by the Joint Photographic Expert Group) compressed image restoration is a significant problem in remote sensing applications. It faces the following two challenges: first, bridging small pixel-value gaps from wide numerical ranges; and second, removing banding artifacts in the condition of lacking available context information. As far as we know, there is currently no research dealing with the above issues. Hence, we develop this initial line of work on JPEG-LS compressed remote sensing image restoration. We propose a novel CNN model called CARNet. Its core idea is a context-aware residual learning mechanism. Specifically, it realizes residual learning for accurate restoration by adopting a scale-invariant baseline. It enables large receptive fields for banding artifact removal through a context-aware scheme. Additionally, it eases the information flow among stages by utilizing a prior-guided feature-fusion mechanism. Alternatively, we design novel R IQA models to provide a better restoration performance assessment for our study by utilizing gradient priors of JPEG-LS banding artifacts. Furthermore, we prepare a new dataset of JPEG-LS compressed remote sensing images to supplement existing benchmark data. Experiments show that our method sets the state-of-the-art for JPEG-LS compressed remote sensing image restoration.<\/jats:p>","DOI":"10.3390\/rs14246318","type":"journal-article","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T02:54:21Z","timestamp":1670986461000},"page":"6318","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["CARNet: Context-Aware Residual Learning for JPEG-LS Compressed Remote Sensing Image Restoration"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7221-6407","authenticated-orcid":false,"given":"Maomei","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information and Technology, Northwest University, Xi\u2019an 710127, China"}]},{"given":"Lei","family":"Tang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Microelectron Technology Institute, Xi\u2019an 710127, China"}]},{"given":"Lijia","family":"Fan","sequence":"additional","affiliation":[{"name":"General Department of Remote Sensing Satellites, China Academy of Space Technology, Beijing 100081, China"}]},{"given":"Sheng","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Information and Technology, Northwest University, Xi\u2019an 710127, China"}]},{"given":"Hangzai","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Information and Technology, Northwest University, Xi\u2019an 710127, China"}]},{"given":"Jinye","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Information and Technology, Northwest University, Xi\u2019an 710127, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,13]]},"reference":[{"key":"ref_1","unstructured":"Yang, M., and Bourbakis, N. (2005, January 7\u201310). An overview of lossless digital image compression techniques. Proceedings of the 48th Midwest Symposium on Circuits and Systems, Covington, KY, USA."},{"key":"ref_2","unstructured":"Weinberger, M.J., Seroussi, G., and Sapiro, G. (April, January 31). LOCO-I: A low complexity, context-based, lossless image compression algorithm. Proceedings of the Proceedings of Data Compression Conference-DCC\u201996, Snowbird, UT, USA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1109\/36.957293","article-title":"Evaluation of JPEG-LS, the new lossless and controlled-lossy still image compression standard, for compression of high-resolution elevation data","volume":"39","author":"Rane","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1109\/TCSVT.2003.815175","article-title":"Adaptive deblocking filter","volume":"13","author":"List","year":"2003","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1109\/TMM.2014.2327563","article-title":"Post-processing for blocking artifact reduction based on inter-block correlation","volume":"16","author":"Yoo","year":"2014","journal-title":"IEEE Trans. Multimed."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1109\/TIP.2007.891788","article-title":"Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images","volume":"16","author":"Foi","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, X., Wu, X., Zhou, J., and Zhao, D. (2015, January 7\u201312). Data-driven sparsity-based restoration of JPEG-compressed images in dual transform-pixel domain. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299153"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, Y., Guo, F., Tan, R.T., and Brown, M.S. (2014, January 6\u201312). A contrast enhancement framework with JPEG artifacts suppression. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10605-2_12"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dong, C., Deng, Y., Loy, C.C., and Tang, X. (2015, January 7\u201313). Compression artifacts reduction by a deep convolutional network. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.73"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, D., Chang, S., Ling, Q., Yang, Y., and Huang, T.S. (2016, January 27\u201330). D3: Deep dual-domain based fast restoration of JPEG-compressed images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.302"},{"key":"ref_11","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_12","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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Galteri, L., Seidenari, L., Bertini, M., and Del Bimbo, A. (2017, January 22\u201329). Deep generative adversarial compression artifact removal. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.517"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yang, W., Hu, Y., and Liu, J. (2018, January 7\u201310). DMCNN: Dual-domain multi-scale convolutional neural network for compression artifacts removal. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451694"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H., and Shao, L. (2021, January 20\u201325). Multi-stage progressive image restoration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"ref_16","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 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/83.855427","article-title":"The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS","volume":"9","author":"Weinberger","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ren, D., Zuo, W., Hu, Q., Zhu, P., and Meng, D. (2019, January 15\u201320). Progressive image deraining networks: A better and simpler baseline. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00406"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"5757","DOI":"10.1109\/TIP.2019.2922850","article-title":"Predicting the quality of images compressed after distortion in two steps","volume":"28","author":"Yu","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","first-page":"230134","article-title":"Reduction of blocking effects in image coding","volume":"23","author":"Reeve","year":"1984","journal-title":"Opt. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/CVPR.2005.38","article-title":"A non-local algorithm for image denoising","volume":"Volume 2","author":"Buades","year":"2005","journal-title":"Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905)"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1109\/TCSVT.2007.906942","article-title":"Efficient image deblocking based on postfiltering in shifted windows","volume":"18","author":"Zhai","year":"2008","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2743","DOI":"10.1109\/TIP.2007.904969","article-title":"Postprocessing of low bit-rate block DCT coded images based on a fields of experts prior","volume":"16","author":"Sun","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4613","DOI":"10.1109\/TIP.2013.2274386","article-title":"Compression artifact reduction by overlapped-block transform coefficient estimation with block similarity","volume":"22","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1109\/TIP.2016.2526910","article-title":"Data-driven soft decoding of compressed images in dual transform-pixel domain","volume":"25","author":"Liu","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","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 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00121"},{"key":"ref_28","unstructured":"Fu, X., Zha, Z.J., Wu, F., Ding, X., and Paisley, J. (November, January 27). Jpeg artifacts reduction via deep convolutional sparse coding. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2131","DOI":"10.1109\/TMM.2019.2895280","article-title":"Deep universal generative adversarial compression artifact removal","volume":"21","author":"Galteri","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_30","unstructured":"Fan, Y., Yu, J., Liu, D., and Huang, T.S. (2020, January 7\u201312). Scale-wise convolution for image restoration. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ehrlich, M., Davis, L., Lim, S.N., and Shrivastava, A. (2020, January 23\u201328). Quantization guided jpeg artifact correction. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58598-3_18"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jiang, J., Zhang, K., and Timofte, R. (2021, January 10\u201317). Towards flexible blind JPEG artifacts removal. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00495"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Fu, X., Huang, J., Zeng, D., Huang, Y., Ding, X., and Paisley, J. (2017, January 21\u201326). Removing rain from single images via a deep detail network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.186"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1109\/TIP.2006.881959","article-title":"A statistical evaluation of recent full reference image quality assessment algorithms","volume":"15","author":"Sheikh","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_37","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. arXiv."},{"key":"ref_38","unstructured":"Independant JPEG Group (1998, March 27). Libjpeg. Available online: http:\/\/libjpeg.sourceforge.net."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Agustsson, E., and Timofte, R. (2017, January 21\u201326). Ntire 2017 challenge on single image super-resolution: Dataset and study. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., and Zhang, L. (2017, January 21\u201326). Ntire 2017 challenge on single image super-resolution: Methods and results. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref_41","unstructured":"Sheikh, H. (2022, November 30). LIVE Image Quality Assessment Database Release 2. Available online: http:\/\/live.ece.utexas.edu\/research\/quality."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6318\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:40:43Z","timestamp":1760146843000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6318"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"references-count":41,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246318"],"URL":"https:\/\/doi.org\/10.3390\/rs14246318","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,13]]}}}