{"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/rethinking-deep-image-prior-for-denoising","title":"Rethinking Deep Image Prior for Denoising","arxiv_id":"2108.12841","date":"2021-08-29","proceeding":"ICCV 2021 10","authors":["Yeonsik Jo","Se Young Chun","Jonghyun Choi"],"abstract":"Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems. Among them, denoising is known to be particularly challenging for the DIP due to noise fitting with the requirement of an early stopping. To address the issue, we first analyze the DIP by the notion of effective degrees of freedom (DF) to monitor the optimization progress and propose a principled stopping criterion before fitting to noise without access of a paired ground truth image for Gaussian noise. We also propose the `stochastic temporal ensemble (STE)' method for incorporating techniques to further improve DIP's performance for denoising. We additionally extend our method to Poisson noise. Our empirical validations show that given a single noisy image, our method denoises the image while preserving rich textual details. Further, our approach outperforms prior arts in LPIPS by large margins with comparable PSNR and SSIM on seven different datasets.","url_abs":"https://arxiv.org/abs/2108.12841v1","url_pdf":"https://arxiv.org/pdf/2108.12841v1.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":"rethinking-deep-image-prior-for-denoising","repo_url":"https://github.com/gistvision/dip-denosing","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"inductive-bias","task_name":"Inductive Bias"},{"task_slug":"ssim","task_name":"SSIM"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2108.12841","atlas_url":"https://app.syntology.ai/?focus=2108.12841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.12841"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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