{"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/pixel-recursive-super-resolution","title":"Pixel Recursive Super Resolution","arxiv_id":"1702.00783","date":"2017-02-02","proceeding":"ICCV 2017 10","authors":["Ryan Dahl","Mohammad Norouzi","Jonathon Shlens"],"abstract":"We present a pixel recursive super resolution model that synthesizes\nrealistic details into images while enhancing their resolution. A low\nresolution image may correspond to multiple plausible high resolution images,\nthus modeling the super resolution process with a pixel independent conditional\nmodel often results in averaging different details--hence blurry edges. By\ncontrast, our model is able to represent a multimodal conditional distribution\nby properly modeling the statistical dependencies among the high resolution\nimage pixels, conditioned on a low resolution input. We employ a PixelCNN\narchitecture to define a strong prior over natural images and jointly optimize\nthis prior with a deep conditioning convolutional network. Human evaluations\nindicate that samples from our proposed model look more photo realistic than a\nstrong L2 regression baseline.","url_abs":"http://arxiv.org/abs/1702.00783v2","url_pdf":"http://arxiv.org/pdf/1702.00783v2.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":"pixel-recursive-super-resolution","repo_url":"https://github.com/abhran/Pixel-Recursive-Super-resolution","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1702.00783","atlas_url":"https://app.syntology.ai/?focus=1702.00783","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1702.00783"}},"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/abhran/Pixel-Recursive-Super-resolution","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"listed":{"samples":1,"ran":1,"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":1,"samples":[{"code_sha256_prefix":"9ed1db0f8b2c5a7b","entry":"lr_scheduler","repo":"abhran/Pixel-Recursive-Super-resolution","repo_kind":"listed","path":"model.py","file_url":"https://github.com/abhran/Pixel-Recursive-Super-resolution/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"9ed1db0f8b2c5a7b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}