{"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/structure-representation-network-and","title":"Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal","arxiv_id":"2210.03061","date":"2022-10-06","proceeding":null,"authors":["Yeying Jin","Wending Yan","Wenhan Yang","Robby T. Tan"],"abstract":"Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog. Dealing with these dense and/or non-uniform distributions can be intractable, since fog's attenuation and airlight (or veiling effect) significantly weaken the background scene information in the input image. To address this problem, we introduce a structure-representation network with uncertainty feedback learning. Specifically, we extract the feature representations from a pre-trained Vision Transformer (DINO-ViT) module to recover the background information. To guide our network to focus on non-uniform fog areas, and then remove the fog accordingly, we introduce the uncertainty feedback learning, which produces the uncertainty maps, that have higher uncertainty in denser fog regions, and can be regarded as an attention map that represents fog's density and uneven distribution. Based on the uncertainty map, our feedback network refines our defogged output iteratively. Moreover, to handle the intractability of estimating the atmospheric light colors, we exploit the grayscale version of our input image, since it is less affected by varying light colors that are possibly present in the input image. The experimental results demonstrate the effectiveness of our method both quantitatively and qualitatively compared to the state-of-the-art methods in handling dense and non-uniform fog or smoke.","url_abs":"https://arxiv.org/abs/2210.03061v1","url_pdf":"https://arxiv.org/pdf/2210.03061v1.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":"structure-representation-network-and","repo_url":"https://github.com/jinyeying/fogremoval","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-dehazing","task_name":"Image Dehazing"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"image-restoration","task_name":"Image Restoration"},{"task_slug":"nonhomogeneous-image-dehazing","task_name":"Nonhomogeneous Image Dehazing"},{"task_slug":"single-image-dehazing","task_name":"Single Image Dehazing"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[{"slug":"smoke","name":"SMOKE","full_name":"Real Dense Non-Uniform Fog"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-dehazing-on-dense-haze","task":"Image Dehazing","dataset":"Dense-Haze","model":"SRDefog","rank_in_archive_order":4,"of":5,"metrics":{"PSNR":"16.67","SSIM":"0.5"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-nh-haze","task":"Image Dehazing","dataset":"NH-HAZE","model":"SRDefog","rank_in_archive_order":3,"of":4,"metrics":{"PSNR":"20.99"},"uses_additional_data":false},{"leaderboard":"/sota/image-dehazing-on-o-haze","task":"Image Dehazing","dataset":"O-Haze","model":"SRDefog","rank_in_archive_order":3,"of":7,"metrics":{"PSNR":"24.61","SSIM":"0.75"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2210.03061","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.03061"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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