{"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/davanet-stereo-deblurring-with-view","title":"DAVANet: Stereo Deblurring with View Aggregation","arxiv_id":"1904.05065","date":"2019-04-10","proceeding":"CVPR 2019 6","authors":["Shangchen Zhou","Jiawei Zhang","WangMeng Zuo","Haozhe Xie","Jinshan Pan","Jimmy Ren"],"abstract":"Nowadays stereo cameras are more commonly adopted in emerging devices such as\ndual-lens smartphones and unmanned aerial vehicles. However, they also suffer\nfrom blurry images in dynamic scenes which leads to visual discomfort and\nhampers further image processing. Previous works have succeeded in monocular\ndeblurring, yet there are few studies on deblurring for stereoscopic images. By\nexploiting the two-view nature of stereo images, we propose a novel stereo\nimage deblurring network with Depth Awareness and View Aggregation, named\nDAVANet. In our proposed network, 3D scene cues from the depth and varying\ninformation from two views are incorporated, which help to remove complex\nspatially-varying blur in dynamic scenes. Specifically, with our proposed\nfusion network, we integrate the bidirectional disparities estimation and\ndeblurring into a unified framework. Moreover, we present a large-scale\nmulti-scene dataset for stereo deblurring, containing 20,637 blurry-sharp\nstereo image pairs from 135 diverse sequences and their corresponding\nbidirectional disparities. The experimental results on our dataset demonstrate\nthat DAVANet outperforms state-of-the-art methods in terms of accuracy, speed,\nand model size.","url_abs":"http://arxiv.org/abs/1904.05065v1","url_pdf":"http://arxiv.org/pdf/1904.05065v1.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":"davanet-stereo-deblurring-with-view","repo_url":"https://github.com/sczhou/DAVANet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"image-deblurring","task_name":"Image Deblurring"}],"methods":[],"datasets_introduced":[{"slug":"davanet","name":"DAVANet","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.05065","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.05065"}},"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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