{"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/enhanced-pix2pix-dehazing-network","title":"Enhanced Pix2pix Dehazing Network","arxiv_id":null,"date":"2019-06-01","proceeding":"CVPR 2019 6","authors":["Yanyun Qu"," Yizi Chen"," Jingying Huang"," Yuan Xie"],"abstract":"In this paper,  we reduce the image dehazing problem to an image-to-image translation problem, and propose  Enhanced Pix2pix Dehazing Network (EPDN),  which generates a haze-free image without relying on the physical scattering model.  EPDN is embedded by a generative adversarial network,  which is followed by a well-designed enhancer.  Inspired by visual perception global-first theory,  the discriminator guides the generator to create a pseudo realistic image on a coarse scale,  while the enhancer following the generator is required to produce a realistic dehazing image on the fine scale.  The enhancer contains two enhancing blocks based on the receptive field model,  which reinforces the dehazing effect in both color and details. The embedded GAN is jointly trained with the enhancer. Extensive experiment results on synthetic datasets and real-world datasets show that the proposed EPDN is superior to the state-of-the-art methods in terms of PSNR,  SSIM,   PI, and subjective visual effect.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2019/html/Qu_Enhanced_Pix2pix_Dehazing_Network_CVPR_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2019/papers/Qu_Enhanced_Pix2pix_Dehazing_Network_CVPR_2019_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":"enhanced-pix2pix-dehazing-network","repo_url":"https://github.com/ErinChen1/EPDN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-dehazing","task_name":"Image Dehazing"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"ssim","task_name":"SSIM"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"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":"dropout","method_name":"Dropout"},{"method_slug":"patchgan","method_name":"PatchGAN"},{"method_slug":"pix2pix","method_name":"Pix2Pix"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-dehazing-on-sots-indoor","task":"Image Dehazing","dataset":"SOTS Indoor","model":"EPDN","rank_in_archive_order":29,"of":34,"metrics":{"PSNR":"25.06","SSIM":"0.9232"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-sots-outdoor","task":"Image Dehazing","dataset":"SOTS Outdoor","model":"EPDN","rank_in_archive_order":30,"of":31,"metrics":{"PSNR":"22.57","SSIM":"0.8630"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}