{"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/a-novel-encoder-decoder-network-with-guided","title":"A Novel Encoder-Decoder Network with Guided Transmission Map for Single Image Dehazing","arxiv_id":"2202.04757","date":"2022-02-08","proceeding":null,"authors":["Le-Anh Tran","Seokyong Moon","Dong-Chul Park"],"abstract":"A novel Encoder-Decoder Network with Guided Transmission Map (EDN-GTM) for single image dehazing scheme is proposed in this paper. The proposed EDN-GTM takes conventional RGB hazy image in conjunction with its transmission map estimated by adopting dark channel prior as the inputs of the network. The proposed EDN-GTM utilizes U-Net for image segmentation as the core network and utilizes various modifications including spatial pyramid pooling module and Swish activation to achieve state-of-the-art dehazing performance. Experiments on benchmark datasets show that the proposed EDN-GTM outperforms most of traditional and deep learning-based image dehazing schemes in terms of PSNR and SSIM metrics. The proposed EDN-GTM furthermore proves its applicability to object detection problems. Specifically, when applied to an image preprocessing tool for driving object detection, the proposed EDN-GTM can efficiently remove haze and significantly improve detection accuracy by 4.73% in terms of mAP measure. The code is available at: https://github.com/tranleanh/edn-gtm.","url_abs":"https://arxiv.org/abs/2202.04757v1","url_pdf":"https://arxiv.org/pdf/2202.04757v1.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":"a-novel-encoder-decoder-network-with-guided","repo_url":"https://github.com/tranleanh/edn-gtm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"image-dehazing","task_name":"Image Dehazing"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"nonhomogeneous-image-dehazing","task_name":"Nonhomogeneous Image Dehazing"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"ssim","task_name":"SSIM"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"single-image-dehazing","task_name":"Single Image Dehazing"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"pyramid-pooling-module","method_name":"Pyramid Pooling Module"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"spatial-pyramid-pooling","method_name":"Spatial Pyramid Pooling"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-dehazing-on-dense-haze","task":"Image Dehazing","dataset":"Dense-Haze","model":"EDN-GTM","rank_in_archive_order":5,"of":5,"metrics":{"PSNR":"15.43","SSIM":"0.5200"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-i-haze","task":"Image Dehazing","dataset":"I-Haze","model":"EDN-GTM","rank_in_archive_order":1,"of":4,"metrics":{"PSNR":"22.90","SSIM":"0.8270"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-o-haze","task":"Image Dehazing","dataset":"O-Haze","model":"EDN-GTM","rank_in_archive_order":6,"of":7,"metrics":{"PSNR":"23.46","SSIM":"0.8198"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}