{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T01:36:14Z","timestamp":1770341774354,"version":"3.49.0"},"reference-count":32,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The high cost and difficulty of collecting real-world foggy scene images mean that automatic driving datasets produce limited images in bad weather and lead to deficient training in automatic driving systems, causing unsafe judgments and leading to traffic accidents. Therefore, to effectively promote the safety and robustness of an autonomous driving system, we improved the CycleGAN model to achieve dataset augmentation of foggy images. Firstly, by combining the self-attention mechanism and the residual network architecture, the sense of hierarchy of the fog effect in the synthesized image was significantly refined. Then, LPIPS was employed to adjust the calculation method for cycle consistency loss to make the synthetic picture more similar to the original one in terms of perception. The experimental results showed that the FID index of the foggy image generated by the improved CycleGAN network was reduced by 3.34, the IS index increased by 15.8%, and the SSIM index increased by 0.1%. The modified method enhances the generation of foggy images, while retaining more details of the original image and reducing content distortion.<\/jats:p>","DOI":"10.3390\/bdcc9040096","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T03:45:23Z","timestamp":1744343123000},"page":"96","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Self-Attention CycleGAN for Unsupervised Image Hazing"],"prefix":"10.3390","volume":"9","author":[{"given":"Hongyin","family":"Ni","sequence":"first","affiliation":[{"name":"School of Computer Science, Northeast Electric Power University, Jilin 132012, China"}]},{"given":"Wanshan","family":"Su","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northeast Electric Power University, Jilin 132012, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"key":"ref_1","first-page":"1133","article-title":"Security testing of visual perception module in autonomous driving system","volume":"59","author":"Wu","year":"2022","journal-title":"J. Comput. Res. Dev."},{"key":"ref_2","unstructured":"Zhang, N., Zhang, L., and Cheng, Z. (2017). Towards simulating foggy and hazy images and evaluating their authenticity. Neural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China, 14\u201318 November 2017, Springer. Proceedings, Part III 24."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"142","DOI":"10.4236\/jcc.2021.910010","article-title":"Domain adaptation for synthesis of hazy images","volume":"9","author":"Sun","year":"2021","journal-title":"J. Comput. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1007\/s11263-018-1072-8","article-title":"Semantic foggy scene understanding with synthetic data","volume":"126","author":"Sakaridis","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"ref_5","first-page":"72","article-title":"Fog simulation method based on depth estimation","volume":"60","author":"Liang","year":"2023","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_6","unstructured":"Dreossi, T., Ghosh, S., Sangiovanni-Vincentelli, A., and Seshia, S.A. (2017). Systematic testing of convolutional neural networks for autonomous driving. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, Y., Zhang, L., Liu, C., and Khurshid, S. (2018, January 3\u20137). Deeproad: Ganbased metamorphic testing and input validation framework for autonomous driving systems. Proceedings of the 33rd ACM\/IEEE International Conference on Automated Software Engineering, Montpellier, France.","DOI":"10.1145\/3238147.3238187"},{"key":"ref_8","first-page":"165","article-title":"Image transformation algorithm of haze scene based on generative adversarial network","volume":"43","author":"Jinsheng","year":"2020","journal-title":"Chin. J. Comput."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"9589","DOI":"10.1007\/s00521-021-05724-1","article-title":"Fdppgan: Remote sensing imagefusion based on deep perceptual patchgan","volume":"33","author":"Pan","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_11","first-page":"1228","article-title":"Chinese font style transfer research based on font features and multi-scale patch generative adversarial network","volume":"45","author":"Cheng","year":"2023","journal-title":"J. YunnanUniv. Nat. Sci. Ed."},{"key":"ref_12","unstructured":"Chen, M., Zhao, S., Liu, H., and Cai, D. (2020, January 7\u201312). Adversarial-learned loss for domain adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., and Smolley, S.P. (2017, January 22\u201329). Least squares generative adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref_14","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_15","first-page":"1","article-title":"Review of attention mechanism in natural language processing","volume":"4","author":"Lei","year":"2020","journal-title":"Data Anal. Knowl. Discov."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s41095-023-0364-2","article-title":"Visual attention network","volume":"9","author":"Guo","year":"2023","journal-title":"Comput. Vis. Media"},{"key":"ref_17","unstructured":"Qin, Z., Sun, W., Deng, H., Li, D., Wei, Y., Lv, B., Yan, J., Kong, L., and Zhong, Y. (2022). cosformer: Rethinking softmax in attention. arXiv."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"e7551","DOI":"10.1002\/cpe.7551","article-title":"Short-term load forecasting of multi-scale recurrent neural networks based on residual structure","volume":"35","author":"Zhao","year":"2023","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_20","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 Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_21","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."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Barron, J.T. (2019, January 15\u201320). A general and adaptive robust loss function. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00446"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast r-cnn. arXiv.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sengupta, A., Ye, Y., Wang, R., Liu, C., and Roy, K. (2019). Going deeper in spiking neural networks: Vgg and residual architectures. Front. Neurosci., 13.","DOI":"10.3389\/fnins.2019.00095"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Menze, M., and Geiger, A. (2015, January 7). Object scene flow for autonomous vehicles. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ancuti, C.O., Ancuti, C., Timofte, R., and De Vleeschouwer, C. (2018, January 18\u201323). O-haze: A dehazing benchmark with real hazy and haze-free outdoor images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00119"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ancuti, C.O., Ancuti, C., Sbert, M., and Timofte, R. (2019, January 22\u201325). Dense-haze: A benchmark for image dehazing with dense-haze and haze-free images. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803046"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ancuti, C., Ancuti, C.O., Timofte, R., and De Vleeschouwer, C. (2018). I-haze: A dehazing benchmark with real hazy and haze-free indoor images. Advanced Concepts for Intelligent Vision Systems: 19th International Conference, ACIVS 2018, Poitiers, France, 24\u201327 September 2018, Proceedings 19; Springer.","DOI":"10.1109\/CVPRW.2018.00119"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ancuti, C.O., Ancuti, C., and Timofte, R. (2020, January 14\u201319). Nh-haze: An image dehazing benchmark with non-homogeneous hazy and haze-free images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00230"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1007\/s00371-016-1305-1","article-title":"Single image dehazing via an improved atmospheric scattering model","volume":"33","author":"Ju","year":"2017","journal-title":"Vis. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109978","DOI":"10.1016\/j.patcog.2023.109978","article-title":"Conditional-pooling for improved data transmission","volume":"145","author":"Bayraktar","year":"2024","journal-title":"Pattern Recognit."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/4\/96\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:12:32Z","timestamp":1760029952000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/4\/96"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,11]]},"references-count":32,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["bdcc9040096"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9040096","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,11]]}}}