{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T10:14:19Z","timestamp":1761732859821,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T00:00:00Z","timestamp":1761436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62061042"],"award-info":[{"award-number":["62061042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Central Guidance of Local Scientific and Technological Development","award":["25ZYJN001"],"award-info":[{"award-number":["25ZYJN001"]}]},{"name":"Gansu Provincial Joint Scientific Research Fund","award":["25JRRA1152"],"award-info":[{"award-number":["25JRRA1152"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>To address global semantic loss, local detail blurring, and spatial\u2013semantic conflict during image restoration under adverse weather conditions, we propose an image restoration network that integrates Mamba with Transformer architectures. We first design a Vision-Mamba\u2013Transformer (VMT) module that combines the long-range dependency modeling of Vision Mamba with the global contextual reasoning of Transformers, facilitating the joint modeling of global structures and local details, thus mitigating information loss and detail blurring during restoration. Second, we introduce an Adaptive Content Guidance (ACG) module that employs dynamic gating and spatial\u2013channel attention to enable effective inter-layer feature fusion, thereby enhancing cross-layer semantic consistency. Finally, we embed the VMT and ACG modules into a U-Net backbone, achieving efficient integration of multi-scale feature modeling and cross-layer fusion, significantly improving reconstruction quality under complex weather conditions. The experimental results show that on Snow100K-S\/L, VMT-Net improves PSNR over the baseline by approximately 0.89 dB and 0.36 dB, with SSIM gains of about 0.91% and 0.11%, respectively. On Outdoor-Rain and Raindrop, it performs similarly to the baseline and exhibits superior detail recovery in real-world scenes. Overall, the method demonstrates robustness and strong detail restoration across diverse adverse-weather conditions.<\/jats:p>","DOI":"10.3390\/jimaging11110376","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T02:23:28Z","timestamp":1761704608000},"page":"376","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adverse-Weather Image Restoration Method Based on VMT-Net"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6547-9847","authenticated-orcid":false,"given":"Zhongmin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}]},{"given":"Xuewen","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China"}]},{"given":"Wenjin","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou 730030, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"129044","DOI":"10.1016\/j.neucom.2024.129044","article-title":"Multiple Adverse Weather Image Restoration: A Review","volume":"618","author":"Xiao","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_2","unstructured":"Valanarasu, J.M.J., Yasarla, R., and Patel, V.M. (2022, January 18\u201324). TransWeather: Transformer-based restoration of images degraded by adverse weather conditions. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA."},{"key":"ref_3","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 (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00837"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11908","DOI":"10.1609\/aaai.v34i07.6865","article-title":"FFA-Net: Feature fusion attention network for single image dehazing","volume":"Volume 34","author":"Qin","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref_5","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 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00416"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, R., Tan, R.T., and Cheong, L.F. (2020, January 13\u201319). All-in-one bad weather removal using architectural search. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00324"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.1109\/TIP.2013.2262284","article-title":"Single image dehazing by multi-scale fusion","volume":"22","author":"Ancuti","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Berman, D., Avidan, S., and Treibitz, M. (2016, January 27\u201330). Non-local image dehazing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.185"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10346","DOI":"10.1109\/TPAMI.2023.3238179","article-title":"Restoring vision in adverse weather conditions with patch-based denoising diffusion models","volume":"45","author":"Legenstein","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, W.T., Huang, Z.K., Tsai, C.C., Yang, H.H., Ding, J.J., and Kuo, S.Y. (2022, January 18\u201324). Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01713"},{"key":"ref_11","unstructured":"Luo, Y., Zhao, R., Wei, X., Chen, J., Lu, Y., Xie, S., Wang, T., Xiong, R., Lu, M., and Zhang, S. (2023). MoWE: Mixture of weather experts for multiple adverse weather removal. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kim, Y., Cho, Y., Nguyen, T.T., Hong, S., and Lee, D. (2023). MetaWeather: Few-shot weather-degraded image restoration. arXiv.","DOI":"10.1007\/978-3-031-73464-9_13"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, X., Li, H., Li, M., and Pan, J. (2023, January 17\u201324). Learning a sparse transformer network for effective image deraining. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00571"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/978-3-031-72670-5_7","article-title":"Restoring images in adverse weather conditions via histogram transformer","volume":"Volume 15475","author":"Sun","year":"2025","journal-title":"Proceedings of the Computer Vision\u2013ECCV 2024"},{"key":"ref_15","unstructured":"Zheng, Z., and Wu, C. (2024). U-shaped vision Mamba for single image dehazing. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., and Timofte, R. (2021, January 11\u201317). SwinIR: Image restoration using Swin Transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., and Yang, M.H. (2022, January 18\u201324). Restormer: Efficient transformer for high-resolution image restoration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-981-96-0911-6_1","article-title":"TANet: Triplet attention network for all-in-one adverse weather image restoration","volume":"Volume 15475","author":"Wang","year":"2024","journal-title":"Proceedings of the Computer Vision\u2014ACCV 2024"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, H., Li, J., Dai, T., Ouyang, Z., Ren, X., and Xia, S.T. (October, January 29). MambaIR: A simple baseline for image restoration with state-space model. Proceedings of the Computer Vision\u2014ECCV 2024, Milan, Italy.","DOI":"10.1007\/978-3-031-72649-1_13"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zou, Z., Hu, Y., and Zhao, F. (2024). FreqMamba: Viewing Mamba from a frequency perspective for image deraining. arXiv.","DOI":"10.1145\/3664647.3680862"},{"key":"ref_21","unstructured":"Weng, J., Yan, Z., Tai, Y., Qian, J., Yang, J., and Li, J. (2024). MambaLLIE: Implicit retinex-aware low light enhancement with global-then-local state space. arXiv."},{"key":"ref_22","unstructured":"Gao, H., Ma, B., Zhang, Y., Yang, J., Yang, J., and Dang, D. (November, January 28). Learning enriched features via selective state spaces model for efficient image deblurring. Proceedings of the 32nd ACM International Conference on Multimedia (MM), Melbourne, Australia."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ren, S., Zhou, D., He, S., Feng, J., and Wang, X. (2022, January 18\u201324). Shunted self-attention via multi-scale token aggregation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01058"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Benesty, J., Chen, J., Huang, Y., and Cohen, I. (2009). Pearson correlation coefficient. Noise Reduction in Speech Processing, Springer.","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3064","DOI":"10.1109\/TIP.2018.2806202","article-title":"DesnowNet: Context-aware deep network for snow removal","volume":"27","author":"Liu","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, R., Cheong, L.F., and Tan, R.T. (2019, January 15\u201320). Heavy rain image restoration: Integrating physics model and conditional adversarial learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00173"},{"key":"ref_27","unstructured":"Loshchilov, I., and Hutter, F. (2017). Decoupled weight decay regularization. arXiv."},{"key":"ref_28","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 (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01255"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1007\/978-3-030-58589-1_45","article-title":"JSTASR: Joint size and transparency-aware snow removal algorithm based on modified partial convolution and veiling effect removal","volume":"Volume 12360","author":"Chen","year":"2020","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2020"},{"key":"ref_30","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 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"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 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":"Qian, R., Tan, R.T., Yang, W., Su, J., and Liu, J. (2018, January 18\u201323). Attentive generative adversarial network for raindrop removal from a single image. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00263"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"12978","DOI":"10.1109\/TPAMI.2022.3183612","article-title":"Image de-raining transformer","volume":"45","author":"Xiao","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","first-page":"17","article-title":"Simple baselines for image restoration","volume":"Volume 13667","author":"Chen","year":"2022","journal-title":"Proceedings of the European Conference on Computer Vision (ECCV)"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Park, D., Lee, B.H., and Chun, S.Y. (2023, January 17\u201324). All-in-One Image Restoration for Unknown Degradations Using Adaptive Discriminative Filters for Specific Degradations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada. Available online: https:\/\/openaccess.thecvf.com\/content\/CVPR2023\/papers\/Park_All-in-One_Image_Restoration_for_Unknown_Degradations_Using_Adaptive_Discriminative_Filters_CVPR_2023_paper.pdf.","DOI":"10.1109\/CVPR52729.2023.00563"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/11\/376\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T03:30:25Z","timestamp":1761708625000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/11\/376"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,26]]},"references-count":35,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["jimaging11110376"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11110376","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,26]]}}}