{"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/end-to-end-interpretable-learning-of-non","title":"End-to-end Interpretable Learning of Non-blind Image Deblurring","arxiv_id":"2007.01769","date":"2020-07-03","proceeding":"ECCV 2020 8","authors":["Thomas Eboli","Jian Sun","Jean Ponce"],"abstract":"Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic splitting method with Richardson fixed-point iterations for its least-squares updates and a proximal operator for the auxiliary variable updates. We propose to precondition the Richardson solver using approximate inverse filters of the (known) blur and natural image prior kernels. Using convolutions instead of a generic linear preconditioner allows extremely efficient parameter sharing across the image, and leads to significant gains in accuracy and/or speed compared to classical FFT and conjugate-gradient methods. More importantly, the proposed architecture is easily adapted to learning both the preconditioner and the proximal operator using CNN embeddings. This yields a simple and efficient algorithm for non-blind image deblurring which is fully interpretable, can be learned end to end, and whose accuracy matches or exceeds the state of the art, quite significantly, in the non-uniform case.","url_abs":"https://arxiv.org/abs/2007.01769v2","url_pdf":"https://arxiv.org/pdf/2007.01769v2.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":"end-to-end-interpretable-learning-of-non","repo_url":"https://github.com/teboli/CPCR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"blind-image-deblurring","task_name":"Blind Image Deblurring"},{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"image-deblurring","task_name":"Image Deblurring"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2007.01769","atlas_url":"https://app.syntology.ai/?focus=2007.01769","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.01769"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/teboli/CPCR","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":8},"by_repo_kind":{"official":{"samples":8,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"f88ea33054e5396b","entry":"conv2d","repo":"teboli/CPCR","repo_kind":"official","path":"conv.py","file_url":"https://github.com/teboli/CPCR/blob/HEAD/conv.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f88ea33054e5396b"}},{"code_sha256_prefix":"a0b069375ef85ea7","entry":"conv2d_color","repo":"teboli/CPCR","repo_kind":"official","path":"conv.py","file_url":"https://github.com/teboli/CPCR/blob/HEAD/conv.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a0b069375ef85ea7"}},{"code_sha256_prefix":"8001c394f51df621","entry":"filt2matrix_largerv1","repo":"teboli/CPCR","repo_kind":"official","path":"kernels.py","file_url":"https://github.com/teboli/CPCR/blob/HEAD/kernels.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8001c394f51df621"}},{"code_sha256_prefix":"8ee63139b0f896d0","entry":"filt2matrix_largerv2","repo":"teboli/CPCR","repo_kind":"official","path":"kernels.py","file_url":"https://github.com/teboli/CPCR/blob/HEAD/kernels.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8ee63139b0f896d0"}},{"code_sha256_prefix":"1bcd4a2f2bffa426","entry":"pad_circular","repo":"teboli/CPCR","repo_kind":"official","path":"conv.py","file_url":"https://github.com/teboli/CPCR/blob/HEAD/conv.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1bcd4a2f2bffa426"}},{"code_sha256_prefix":"07da98b519804411","entry":"psf2otf","repo":"teboli/CPCR","repo_kind":"official","path":"kernels.py","file_url":"https://github.com/teboli/CPCR/blob/HEAD/kernels.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"07da98b519804411"}},{"code_sha256_prefix":"a2b2d1464b26b597","entry":"psnr","repo":"teboli/CPCR","repo_kind":"official","path":"loss.py","file_url":"https://github.com/teboli/CPCR/blob/HEAD/loss.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a2b2d1464b26b597"}},{"code_sha256_prefix":"c1384711ea594ee0","entry":"ssim","repo":"teboli/CPCR","repo_kind":"official","path":"loss.py","file_url":"https://github.com/teboli/CPCR/blob/HEAD/loss.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c1384711ea594ee0"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}