{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T11:01:50Z","timestamp":1787655710195,"version":"build-2736575974"},"reference-count":34,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ningbo Science and Technology innovation 2025 major project","award":["2021Z010"],"award-info":[{"award-number":["2021Z010"]}]},{"name":"Ningbo Science and Technology innovation 2025 major project","award":["2021Z063"],"award-info":[{"award-number":["2021Z063"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features. Single image dehazing is one of the most important tasks in the field of image restoration. In recent years, due to the progress of deep learning, single image dehazing has made great progress. With the success of Transformer in advanced computer vision tasks, some research studies also began to apply Transformer to image dehazing tasks and obtained surprising results. However, both the deconvolution-neural-network-based dehazing algorithm and Transformer based dehazing algorithm magnify their advantages and disadvantages separately. Therefore, this paper proposes a novel Transformer\u2013Convolution fusion dehazing network (TCFDN), which uses Transformer\u2019s global modeling ability and convolutional neural network\u2019s local modeling ability to improve the dehazing ability. In the Transformer\u2013Convolution fusion dehazing network, the classic self-encoder structure is used. This paper proposes a Transformer\u2013Convolution hybrid layer, which uses an adaptive fusion strategy to make full use of the Swin-Transformer and convolutional neural network to extract and reconstruct image features. On the basis of previous research, this layer further improves the ability of the network to remove haze. A series of contrast experiments and ablation experiments not only proved that the Transformer\u2013Convolution fusion dehazing network proposed in this paper exceeded the more advanced dehazing algorithm, but also provided solid and powerful evidence for the basic theory on which it depends.<\/jats:p>","DOI":"10.3390\/s23010043","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T05:42:53Z","timestamp":1671601373000},"page":"43","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Efficient Dehazing Algorithm Based on the Fusion of Transformer and Convolutional Neural Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Jun","family":"Xu","sequence":"first","affiliation":[{"name":"Wenzhou Mass Transit Railway Investment Group Co., Ltd., Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi-Xuan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1785-7847","authenticated-orcid":false,"given":"Zhe-Ming","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1109\/TIP.2015.2499698","article-title":"Multi-Scale Patch-Based Image Restoration","volume":"25","author":"Papyan","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1109\/TMM.2019.2902097","article-title":"Quality Evaluation of Image Dehazing Methods Using Synthetic Hazy Images","volume":"21","author":"Min","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2682","DOI":"10.1109\/TGRS.2015.2504369","article-title":"An Iterative Haze Optimized Transformation for Automatic Cloud\/Haze Detection of Landsat Imagery","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_5","first-page":"1910","article-title":"Blind Image Blur Estimation via Deep Learning","volume":"25","author":"Yan","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2017.09.003","article-title":"Haze Visibility Enhancement: A Survey and Quantitative Benchmarking","volume":"165","author":"Lu","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Engin, D., Gen\u00e7, A., and Ekenel, H.K. (2018, January 18\u201322). Cycle-dehaze:Enhanced Cyclegan for Single Image Dehazing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00127"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Tan, R.T. (2008, January 23\u201328). Visibility in Bad Weather From A Single Image. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587643"},{"key":"ref_10","first-page":"2341","article-title":"Single Image Haze Removal Using Dark Channel Prior","volume":"33","author":"He","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Berman, D., and Avidan, S. (2016, January 27\u201330). Non-local Image Dehazing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.185"},{"key":"ref_12","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_13","unstructured":"Liu, X., Ma, Y., Shi, Z., and Chen, J. (November, January 27). Griddehazenet: Attention-based Multi-scale Network for Image Dehazing. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5187","DOI":"10.1109\/TIP.2016.2598681","article-title":"DehazeNet: An End-to-end System for Single Image Haze Removal","volume":"25","author":"Cai","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., and Yang, M.H. (2016, January 11\u201314). Single Image Dehazing via Multi-scale Convolutional Neural Networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_10"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, B., Peng, X., Wang, Z., Xu, J., and Feng, D. (2017, January 22\u201329). Aod-net: All-in-one Dehazing Network. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.511"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Patel, V.M. (2018, January 18\u201323). Densely Connected Pyramid Dehazing Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00337"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_20","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the Twenty-Eighth Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ren, W., Ma, L., Zhang, J., Pan, J., Cao, X., Liu, M., and Yang, M. (2018, January 18\u201323). Gated Fusion Network for Single Image Dehazing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00343"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fourure, D., Emonet, R., Fromont, E., Muselet, D., Tremeau, A., and Wolf, C. (2017, January 4\u20137). Residual Conv-deconv Grid Network for Semantic Segmentation. Proceedings of the British Machine Vision Conference, London, UK.","DOI":"10.5244\/C.31.181"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hong, M., Xie, Y., Li, C., and Qu, Y. (2020, January 13\u201319). Distilling Image Dehazing with Heterogeneous Task Imitation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00352"},{"key":"ref_24","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, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2692","DOI":"10.1109\/TIP.2019.2952032","article-title":"Unsupervised Single Image Dehazing Using Dark Channel Prior Loss","volume":"29","author":"Golts","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wu, H., Qu, Y., Lin, S., Zhou, J., Qiao, R., Zhang, Z., Xie, Y., and Ma, L. (2021, January 20\u201325). Contrastive Learning for Compact Single Image Dehazing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01041"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Song, Y., He, Z., Qian, H., and Du, X. (2022). Vision Transformers for Single Image Dehazing. arXiv.","DOI":"10.1109\/TIP.2023.3256763"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, K., Li, Y., Liang, J., Cao, J., Zhang, Y., Tang, H., Timofte, R., and van Gool, L. (2022). Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis. arXiv.","DOI":"10.1007\/s11633-023-1466-0"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Su, Z., Zhang, H., Chen, J., Pang, L., Ngo, C.W., and Jiang, Y.G. (2022). Adaptive Split-Fusion Transformer. arXiv.","DOI":"10.1109\/ICME55011.2023.00204"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Qin, X., Wang, Z., Bai, Y., Xie, X., and Jia, H. (2020, January 7\u201312). FFA-Net: Feature Fusion Attention Network for Single Image Dehazing. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6865"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TIP.2018.2867951","article-title":"Benchmarking Single-image Dehazing and Beyond","volume":"28","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dong, H., Pan, J., Xiang, L., Hu, Z., Zhang, X., Wang, F., and Yang, M.H. (2020, January 13\u201319). Multi-scale Boosted Dehazing Network with Dense Feature Fusion. Proceedings of the IEEE\/CVF International Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00223"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dong, J., and Pan, J. (2020, January 23\u201328). Physics-based Feature Dehazing Networks. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58577-8_12"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/43\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:45:13Z","timestamp":1760147113000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,21]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010043"],"URL":"https:\/\/doi.org\/10.3390\/s23010043","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,21]]}}}