{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:27:07Z","timestamp":1760149627631,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T00:00:00Z","timestamp":1691712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Huanggang Normal University","award":["2042023009"],"award-info":[{"award-number":["2042023009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Images captured during rainy days present the challenge of maintaining a symmetrical balance between foreground elements (like rain streaks) and the background scenery. The interplay between these rain-obscured images is reminiscent of the principle of symmetry, where one element, the rain streak, overshadows or disrupts the visual quality of the entire image. The challenge lies not just in eradicating the rain streaks but in ensuring the background is symmetrically restored to its original clarity. Recently, numerous deraining algorithms that employ deep learning techniques have been proposed, demonstrating promising results. Yet, achieving a perfect symmetrical balance by effectively removing rain streaks from a diverse set of images, while also symmetrically restoring the background details, is a monumental task. To address this issue, we introduce an image-deraining algorithm that leverages multi-scale dilated residual recurrent networks. The algorithm begins by utilizing convolutional activation layers to symmetrically process both the foreground and background features. Then, to ensure the symmetrical dissemination of the characteristics of rain streaks and the background, it employs long short-term memory networks in conjunction with gated recurrent units across various stages. The algorithm then incorporates dilated residual blocks (DRB), composed of dilated convolutions with three distinct dilation factors. This integration expands the receptive field, facilitating the extraction of deep, multi-scale features of both the rain streaks and background information. Furthermore, considering the complex and diverse nature of rain streaks, a channel attention (CA) mechanism is incorporated to capture richer image features and enhance the model\u2019s performance. Ultimately, convolutional layers are employed to fuse the image features, resulting in a derained image. An evaluation encompassing seven benchmark datasets, assessed using five quality metrics against various conventional and modern algorithms, confirms the robustness and flexibility of our approach.<\/jats:p>","DOI":"10.3390\/sym15081571","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T10:20:16Z","timestamp":1691749216000},"page":"1571","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Symmetric Enhancement of Visual Clarity through a Multi-Scale Dilated Residual Recurrent Network Approach for Image Deraining"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4896-7205","authenticated-orcid":false,"given":"Jameel Ahmed","family":"Bhutto","sequence":"first","affiliation":[{"name":"School of Computer, Huanggang Normal University, Huanggang 438000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruihong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer, Huanggang Normal University, Huanggang 438000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziaur","family":"Rahman","sequence":"additional","affiliation":[{"name":"School of Computer, Huanggang Normal University, Huanggang 438000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1049\/ipr2.12347","article-title":"A comprehensive survey: Image deraining and stereo-matching task-driven performance analysis","volume":"16","author":"Du","year":"2022","journal-title":"Iet Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109038","DOI":"10.1109\/ACCESS.2020.3001206","article-title":"Efficient image enhancement model for correcting uneven illumination images","volume":"8","author":"Rahman","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Rahman, Z., Aamir, M., Pu, Y.F., Ullah, F., and Dai, Q. (2018). A smart system for low-light image enhancement with color constancy and detail manipulation in complex light environments. Symmetry, 10.","DOI":"10.3390\/sym10120718"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7404","DOI":"10.1109\/TIP.2021.3102504","article-title":"Rain-free and residue hand-in-hand: A progressive coupled network for real-time image deraining","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rahman, Z., Aamir, M., Ali, Z., Saudagar, A.K.J., AlTameem, A., and Muhammad, K. (2023). Efficient Contrast Adjustment and Fusion Method for Underexposed Images in Industrial Cyber-Physical Systems. IEEE Syst. J.","DOI":"10.1109\/JSYST.2023.3262593"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2214","DOI":"10.3390\/app11052214","article-title":"Rain streak removal for single images using conditional generative adversarial networks","volume":"11","author":"Hettiarachchi","year":"2021","journal-title":"Appl. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.patrec.2018.10.006","article-title":"Single image rain removal based on depth of field and sparse coding","volume":"116","author":"Xiao","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3874","DOI":"10.1109\/TIP.2017.2708841","article-title":"Single image rain streak decomposition using layer priors","volume":"26","author":"Li","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1109\/TIP.2018.2880512","article-title":"Fastderain: A novel video rain streak removal method using directional gradient priors","volume":"28","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2944","DOI":"10.1109\/TIP.2017.2691802","article-title":"Clearing the skies: A deep network architecture for single-image rain removal","volume":"26","author":"Fu","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shafiq, M., and Gu, Z. (2022). Deep residual learning for image recognition: A survey. Appl. Sci., 12.","DOI":"10.3390\/app12188972"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, P., Jin, J., Jin, G., and Fan, L. (2022). Scale-Space Feature Recalibration Network for Single Image Deraining. Sensors, 22.","DOI":"10.3390\/s22186823"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Patel, V.M. (2018, January 18\u201323). Density-aware single image de-raining using a multi-stream dense network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00079"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3943","DOI":"10.1109\/TCSVT.2019.2920407","article-title":"Image de-raining using a conditional generative adversarial network","volume":"30","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wei, B., Wang, D., Wang, Z., and Zhang, L. (2022). PRAGAN: Progressive Recurrent Attention GAN with Pretrained ViT Discriminator for Single-Image Deraining. Sensors, 22.","DOI":"10.3390\/s22249587"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, Y., Tan, R.T., Guo, X., Lu, J., and Brown, M.S. (2016, January 27\u201330). Rain streak removal using layer priors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.299"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6573","DOI":"10.1109\/TIE.2017.2682036","article-title":"Error-optimized sparse representation for single image rain removal","volume":"64","author":"Chen","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, D.H., Ahn, W.J., Lim, M.T., Kang, T.K., and Kim, D.W. (2021). Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder. Appl. Sci., 11.","DOI":"10.3390\/app11062873"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Yang, W., Tan, R.T., Feng, J., Liu, J., Guo, Z., and Yan, S. (2017, January 21\u201326). Deep joint rain detection and removal from a single image. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.183"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yu, X., Zhang, G., Tan, F., Li, F., and Xie, W. (2023). Progressive Hybrid-Modulated Network for Single Image Deraining. Mathematics, 11.","DOI":"10.3390\/math11030691"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, X., Wu, J., Lin, Z., Liu, H., and Zha, H. (2018, January 8\u201314). Recurrent squeeze-and-excitation context aggregation net for single image deraining. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_16"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jiang, K., Wang, Z., Yi, P., Chen, C., Huang, B., Luo, Y., Ma, J., and Jiang, J. (2020, January 13\u201319). Multi-scale progressive fusion network for single image deraining. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00837"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103039","DOI":"10.1016\/j.jvcir.2021.103039","article-title":"Single image deraining using context aggregation recurrent network","volume":"75","author":"Tang","year":"2021","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, X., Huang, Y., and Xu, L. (2021, January 20\u201325). Multi-scale hourglass hierarchical fusion network for single image deraining. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00097"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, S., Xu, Y., Ren, M., Yang, Y., and Wan, W. (2022). Rain Removal of Single Image Based on Directional Gradient Priors. Appl. Sci., 12.","DOI":"10.3390\/app122211628"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Son, C.H., and Jeong, D.H. (2022). Heavy Rain Face Image Restoration: Integrating Physical Degradation Model and Facial Component-Guided Adversarial Learning. Sensors, 22.","DOI":"10.3390\/s22145359"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6843","DOI":"10.1109\/TIP.2021.3099396","article-title":"A two-stage density-aware single image deraining method","volume":"30","author":"Cao","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s11760-021-01972-9","article-title":"DerainAttentionGAN: Unsupervised single-image deraining using attention-guided generative adversarial networks","volume":"16","author":"Guo","year":"2022","journal-title":"Signal Image Video Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1049\/ipr2.12127","article-title":"Iterative multi-scale residual network for deblurring","volume":"15","author":"Zhang","year":"2021","journal-title":"IET Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"53942","DOI":"10.1109\/ACCESS.2020.2980996","article-title":"Multi-scale neural network with dilated convolutions for image deblurring","volume":"8","author":"Ople","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, T., Yang, X., Xu, K., Chen, S., Zhang, Q., and Lau, R.W. (2019, January 15\u201320). Spatial attentive single-image deraining with a high quality real rain dataset. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01255"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wei, W., Meng, D., Zhao, Q., Xu, Z., and Wu, Y. (2019, January 15\u201320). Semi-supervised transfer learning for image rain removal. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00400"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, S., Araujo, I.B., Ren, W., Wang, Z., Tokuda, E.K., Junior, R.H., Cesar-Junior, R., Zhang, J., Guo, X., and Cao, X. (2019, January 15\u201320). Single image deraining: A comprehensive benchmark analysis. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00396"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"116087","DOI":"10.1016\/j.eswa.2021.116087","article-title":"Structural similarity index (SSIM) revisited: A data-driven approach","volume":"189","author":"Bakurov","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.neucom.2021.08.048","article-title":"VP-NIQE: An opinion-unaware visual perception natural image quality evaluator","volume":"463","author":"Wu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.image.2014.06.006","article-title":"No-reference image quality assessment based on spatial and spectral entropies","volume":"29","author":"Liu","year":"2014","journal-title":"Signal Process. Image Commun."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18\u201323). The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fan, Z., Wu, H., Fu, X., Huang, Y., and Ding, X. (2018, January 22\u201326). Residual-guide network for single image deraining. Proceedings of the 26th ACM International Conference on Multimedia, Seoul, Republic of Korea.","DOI":"10.1145\/3240508.3240694"},{"key":"ref_41","unstructured":"Ba, Y., Zhang, H., Yang, E., Suzuki, A., Pfahnl, A., Chandrappa, C.C., de Melo, C.M., You, S., Soatto, S., and Wong, A. (2012, January 7\u201313). Not just streaks: Towards ground truth for single image deraining. Proceedings of the European Conference on Computer Vision, Florence, Italy."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"9534","DOI":"10.1109\/TPAMI.2023.3241756","article-title":"Continual image deraining with hypergraph convolutional networks","volume":"45","author":"Fu","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/TIP.2022.3232943","article-title":"TRNR: Task-Driven Image Rain and Noise Removal With a Few Images Based on Patch Analysis","volume":"32","author":"Ran","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1016\/j.apm.2018.03.001","article-title":"A directional global sparse model for single image rain removal","volume":"59","author":"Deng","year":"2018","journal-title":"Appl. Math. Model."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Nanba, Y., Miyata, H., and Han, X.H. (2022, January 18\u201324). Dual heterogeneous complementary networks for single image deraining. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00072"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"103740","DOI":"10.1016\/j.dsp.2022.103740","article-title":"DPNet: Detail-preserving image deraining via learning frequency domain knowledge","volume":"130","author":"Yang","year":"2022","journal-title":"Digit. Signal Process."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tejaswini, M., Sumanth, T.H., and Naik, K.J. (2023). Single image deraining using modified bilateral recurrent network (modified_BRN). Multimed. Tools Appl., 1\u201324.","DOI":"10.1007\/s11042-023-15276-2"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.neucom.2021.10.029","article-title":"Single image rain removal using recurrent scale-guide networks","volume":"467","author":"Wang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1109\/TNNLS.2019.2926481","article-title":"Lightweight pyramid networks for image deraining","volume":"31","author":"Fu","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"023022","DOI":"10.1117\/1.JEI.31.2.023022","article-title":"Contrastive learning-based generative network for single image deraining","volume":"31","author":"Du","year":"2022","journal-title":"J. Electron. Imaging"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, H., Yue, Z., Xie, Q., Zhao, Q., Zheng, Y., and Meng, D. (2021, January 20\u201325). From rain generation to rain removal. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01455"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"14013","DOI":"10.1007\/s00521-022-07226-0","article-title":"Mltdnet: An efficient multi-level transformer network for single image deraining","volume":"34","author":"Gao","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"109294","DOI":"10.1016\/j.patcog.2022.109294","article-title":"Recurrent wavelet structure-preserving residual network for single image deraining","volume":"137","author":"Hsu","year":"2023","journal-title":"Pattern Recognit."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/8\/1571\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:31:45Z","timestamp":1760128305000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/8\/1571"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,11]]},"references-count":53,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["sym15081571"],"URL":"https:\/\/doi.org\/10.3390\/sym15081571","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2023,8,11]]}}}