{"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/flare-free-vision-empowering-uformer-with","title":"Flare-Free Vision: Empowering Uformer with Depth Insights","arxiv_id":null,"date":"2024-04-01","proceeding":"ICASSP 2024 4","authors":["Yousef Kotp","Marwan Torki"],"abstract":"Image flare is a common problem that occurs when a camera lens is pointed at a strong light source. It can manifest as ghosting, blooming, or other artifacts that can degrade the image quality. We propose a novel deep learning approach for flare removal that uses a combination of depth estimation and image restoration. We use a Dense Vision Transformer to estimate the depth of the scene. This depth map is then concatenated to the input image, which is then fed into a Uformer, a general U-shaped transformer for image restoration. Our proposed method demonstrates state-of-the-art performance on the Flare7K++ test dataset, demonstrating its effectiveness in removing flare artifacts from images. Our approach also demonstrates robustness and generalization to real-world images with various types of flare. We believe that our work opens up new possibilities for using depth information for image restoration. The code is available on GitHub","url_abs":"https://ieeexplore.ieee.org/document/10446006","url_pdf":"https://www.researchgate.net/publication/376586936_FLARE-FREE_VISION_EMPOWERING_UFORMER_WITH_DEPTH_INSIGHTS_ICASSP2024","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":"flare-free-vision-empowering-uformer-with","repo_url":"https://github.com/yousefkotp/Flare-Free-Vision-Empowering-Uformer-with-Depth-Insights","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"flare-removal","task_name":"Flare Removal"},{"task_slug":"image-restoration","task_name":"Image Restoration"}],"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":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/flare-removal-on-flare7k","task":"Flare Removal","dataset":"Flare7K","model":"Kotp et al","rank_in_archive_order":1,"of":10,"metrics":{"LPIPS":"0.0422","PSNR":"27.662","SSIM":"0.897"},"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}