{"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/blind-face-restoration-via-deep-multi-scale","title":"Blind Face Restoration via Deep Multi-scale Component Dictionaries","arxiv_id":"2008.00418","date":"2020-08-02","proceeding":"ECCV 2020 8","authors":["Xiaoming Li","Chaofeng Chen","Shangchen Zhou","Xianhui Lin","WangMeng Zuo","Lei Zhang"],"abstract":"Recent reference-based face restoration methods have received considerable attention due to their great capability in recovering high-frequency details on real low-quality images. However, most of these methods require a high-quality reference image of the same identity, making them only applicable in limited scenes. To address this issue, this paper suggests a deep face dictionary network (termed as DFDNet) to guide the restoration process of degraded observations. To begin with, we use K-means to generate deep dictionaries for perceptually significant face components (\\ie, left/right eyes, nose and mouth) from high-quality images. Next, with the degraded input, we match and select the most similar component features from their corresponding dictionaries and transfer the high-quality details to the input via the proposed dictionary feature transfer (DFT) block. In particular, component AdaIN is leveraged to eliminate the style diversity between the input and dictionary features (\\eg, illumination), and a confidence score is proposed to adaptively fuse the dictionary feature to the input. Finally, multi-scale dictionaries are adopted in a progressive manner to enable the coarse-to-fine restoration. Experiments show that our proposed method can achieve plausible performance in both quantitative and qualitative evaluation, and more importantly, can generate realistic and promising results on real degraded images without requiring an identity-belonging reference. The source code and models are available at \\url{https://github.com/csxmli2016/DFDNet}.","url_abs":"https://arxiv.org/abs/2008.00418v1","url_pdf":"https://arxiv.org/pdf/2008.00418v1.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":"blind-face-restoration-via-deep-multi-scale","repo_url":"https://github.com/csxmli2016/DFDNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"blind-face-restoration","task_name":"Blind Face Restoration"},{"task_slug":"video-super-resolution","task_name":"Video Super-Resolution"}],"methods":[{"method_slug":"dfdnet","method_name":"DFDNet"}],"datasets_introduced":[],"methods_introduced":[{"slug":"dfdnet","name":"DFDNet","full_name":"DFDNet"}],"results":[{"leaderboard":"/sota/video-super-resolution-on-msu-vsr-benchmark","task":"Video Super-Resolution","dataset":"MSU Video Super Resolution Benchmark: Detail Restoration","model":"DFDnet","rank_in_archive_order":32,"of":32,"metrics":{"1 - LPIPS":"0.623","ERQAv1.0":"0.339","FPS":"0.909","PSNR":"24.832","QRCRv1.0":"0","SSIM":"0.759","Subjective score":"0.277"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2008.00418","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.00418"}},"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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