{"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/jigsawnet-shredded-image-reassembly-using","title":"JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition","arxiv_id":"1809.04137","date":"2018-09-11","proceeding":null,"authors":["Canyu Le","Xin Li"],"abstract":"This paper proposes a novel algorithm to reassemble an arbitrarily shredded\nimage to its original status. Existing reassembly pipelines commonly consist of\na local matching stage and a global compositions stage. In the local stage, a\nkey challenge in fragment reassembly is to reliably compute and identify\ncorrect pairwise matching, for which most existing algorithms use handcrafted\nfeatures, and hence, cannot reliably handle complicated puzzles. We build a\ndeep convolutional neural network to detect the compatibility of a pairwise\nstitching, and use it to prune computed pairwise matches. To improve the\nnetwork efficiency and accuracy, we transfer the calculation of CNN to the\nstitching region and apply a boost training strategy. In the global composition\nstage, we modify the commonly adopted greedy edge selection strategies to two\nnew loop closure based searching algorithms. Extensive experiments show that\nour algorithm significantly outperforms existing methods on solving various\npuzzles, especially those challenging ones with many fragment pieces.","url_abs":"http://arxiv.org/abs/1809.04137v1","url_pdf":"http://arxiv.org/pdf/1809.04137v1.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":"jigsawnet-shredded-image-reassembly-using","repo_url":"https://github.com/Lecanyu/JigsawNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"jigsawnet-shredded-image-reassembly-using","repo_url":"https://github.com/LONG-9621/Image_stitching02","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"jigsawnet-shredded-image-reassembly-using","repo_url":"https://github.com/LevidRodriguez/JigsawNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.04137","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.04137"}},"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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