{"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/patchnr-learning-from-small-data-by-patch","title":"PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization","arxiv_id":"2205.12021","date":"2022-05-24","proceeding":null,"authors":["Fabian Altekrüger","Alexander Denker","Paul Hagemann","Johannes Hertrich","Peter Maass","Gabriele Steidl"],"abstract":"Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications. In this paper, we introduce a powerful regularizer for the variational modeling of inverse problems in imaging. Our regularizer, called patch normalizing flow regularizer (patchNR), involves a normalizing flow learned on small patches of very few images. In particular, the training is independent of the considered inverse problem such that the same regularizer can be applied for different forward operators acting on the same class of images. By investigating the distribution of patches versus those of the whole image class, we prove that our model is indeed a MAP approach. Numerical examples for low-dose and limited-angle computed tomography (CT) as well as superresolution of material images demonstrate that our method provides very high quality results. The training set consists of just six images for CT and one image for superresolution. Finally, we combine our patchNR with ideas from internal learning for performing superresolution of natural images directly from the low-resolution observation without knowledge of any high-resolution image.","url_abs":"https://arxiv.org/abs/2205.12021v3","url_pdf":"https://arxiv.org/pdf/2205.12021v3.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":"patchnr-learning-from-small-data-by-patch","repo_url":"https://github.com/fabianaltekrueger/patchnr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2205.12021","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.12021"}},"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. 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/fabianaltekrueger/patchnr","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":5},"by_repo_kind":{"official":{"samples":5,"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":"aed48d89e94f836b","entry":"Downsample","repo":"fabianaltekrueger/patchnr","repo_kind":"official","path":"patchNR_zeroshot.py","file_url":"https://github.com/fabianaltekrueger/patchnr/blob/HEAD/patchNR_zeroshot.py","link_basis":"harvester_set","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":"aed48d89e94f836b"}},{"code_sha256_prefix":"ad0580d02dd64705","entry":"Downsample","repo":"fabianaltekrueger/patchnr","repo_kind":"official","path":"patchNR_zeroshot_material.py","file_url":"https://github.com/fabianaltekrueger/patchnr/blob/HEAD/patchNR_zeroshot_material.py","link_basis":"harvester_set","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":"ad0580d02dd64705"}},{"code_sha256_prefix":"c1656500b37fde00","entry":"imread","repo":"fabianaltekrueger/patchnr","repo_kind":"official","path":"utils.py","file_url":"https://github.com/fabianaltekrueger/patchnr/blob/HEAD/utils.py","link_basis":"harvester_set","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":"c1656500b37fde00"}},{"code_sha256_prefix":"016f10ff0b01659f","entry":"patchNR","repo":"fabianaltekrueger/patchnr","repo_kind":"official","path":"patchNR_zeroshot.py","file_url":"https://github.com/fabianaltekrueger/patchnr/blob/HEAD/patchNR_zeroshot.py","link_basis":"harvester_set","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":"016f10ff0b01659f"}},{"code_sha256_prefix":"23264102b960ea38","entry":"patchNR","repo":"fabianaltekrueger/patchnr","repo_kind":"official","path":"patchNR_zeroshot_material.py","file_url":"https://github.com/fabianaltekrueger/patchnr/blob/HEAD/patchNR_zeroshot_material.py","link_basis":"harvester_set","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":"23264102b960ea38"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}