{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dehazedct-towards-effective-non-homogeneous","title":"DehazeDCT: Towards Effective Non-Homogeneous Dehazing via Deformable Convolutional Transformer","arxiv_id":null,"date":"2024-06-12","proceeding":"CVPRW 2024 6","authors":["Wei Dong","Han Zhou","Ruiyi Wang","Xiaohong Liu","Guangtao Zhai","Jun Chen"],"abstract":"Image dehazing, a pivotal task in low-level vision, aims to restore the visibility and detail from hazy images. Many deep learning methods with powerful representation learning capability demonstrate advanced performance on non-homogeneous dehazing, however, these methods usually struggle with processing high-resolution\r\nimages (e.g., 4000 × 6000) due to their heavy computational demands. To address these challenges, we introduce an innovative non-homogeneous Dehazing method via Deformable Convolutional Transformer-like architecture\r\n(DehazeDCT). Specifically, we first design a transformerlike network based on deformable convolution v4, which offers long-range dependency and adaptive spatial aggregation capabilities and demonstrates faster convergence and forward speed. Furthermore, we leverage a lightweight Retinex-inspired transformer to achieve color correction and structure refinement. Extensive experiment results and highly competitive performance of our method in NTIRE 2024 Dense and Non-Homogeneous Dehazing Challenge, ranking second among all 16 submissions, demonstrate the superior capability of our proposed method. The code is available: https://github.com/movingforward100/Dehazing_R.","url_abs":"https://github.com/movingforward100/Dehazing_R","url_pdf":"https://openaccess.thecvf.com/content/CVPR2024W/NTIRE/papers/Dong_DehazeDCT_Towards_Effective_Non-Homogeneous_Dehazing_via_Deformable_Convolutional_Transformer_CVPRW_2024_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dehazedct-towards-effective-non-homogeneous","repo_url":"https://github.com/movingforward100/Dehazing_R","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-dehazing","task_name":"Image Dehazing"},{"task_slug":"nonhomogeneous-image-dehazing","task_name":"Nonhomogeneous Image Dehazing"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"single-image-dehazing","task_name":"Single Image Dehazing"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"deformable-convolution","method_name":"Deformable Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-dehazing-on-dnh-haze","task":"Image Dehazing","dataset":"DNH-HAZE","model":"DehazeDCT","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"21.73","SSIM":"0.743"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-hd-nh-haze","task":"Image Dehazing","dataset":"HD-NH-HAZE","model":"DehazeDCT","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"22.36","SSIM":"0.752"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-nh-haze2","task":"Image Dehazing","dataset":"NH-HAZE2","model":"DehazeDCT","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"22.86","SSIM":"0.877"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-dehazing-on-dnh-haze","task":"Single Image Dehazing","dataset":"DNH-HAZE","model":"DehazeDCT","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"21.73","SSIM":"0.743"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-dehazing-on-hd-nh-haze","task":"Single Image Dehazing","dataset":"HD-NH-HAZE","model":"DehazeDCT","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"22.36","SSIM":"0.752"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-dehazing-on-nh-haze","task":"Single Image Dehazing","dataset":"NH-HAZE","model":"DehazeDCT","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"22.78"},"uses_additional_data":false},{"leaderboard":"/sota/single-image-dehazing-on-nh-haze2","task":"Single Image Dehazing","dataset":"NH-HAZE2","model":"DehazeDCT","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"22.86","SSIM":"0.877"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}