{"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/pi-rec-progressive-image-reconstruction-1","title":"PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain","arxiv_id":"1903.10146","date":"2019-03-25","proceeding":"arXiv 2019 3","authors":["Sheng You","Ning You","Minxue Pan"],"abstract":"We propose a universal image reconstruction method to represent detailed\nimages purely from binary sparse edge and flat color domain. Inspired by the\nprocedures of painting, our framework, based on generative adversarial network,\nconsists of three phases: Imitation Phase aims at initializing networks,\nfollowed by Generating Phase to reconstruct preliminary images. Moreover,\nRefinement Phase is utilized to fine-tune preliminary images into final outputs\nwith details. This framework allows our model generating abundant high\nfrequency details from sparse input information. We also explore the defects of\ndisentangling style latent space implicitly from images, and demonstrate that\nexplicit color domain in our model performs better on controllability and\ninterpretability. In our experiments, we achieve outstanding results on\nreconstructing realistic images and translating hand drawn drafts into\nsatisfactory paintings. Besides, within the domain of edge-to-image\ntranslation, our model PI-REC outperforms existing state-of-the-art methods on\nevaluations of realism and accuracy, both quantitatively and qualitatively.","url_abs":"http://arxiv.org/abs/1903.10146v1","url_pdf":"http://arxiv.org/pdf/1903.10146v1.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":"pi-rec-progressive-image-reconstruction-1","repo_url":"https://github.com/youyuge34/PI-REC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-reconstruction-on-edge-to-handbags","task":"Image Reconstruction","dataset":"Edge-to-Handbags","model":"PI-REC","rank_in_archive_order":1,"of":4,"metrics":{"FID":"0.069","HP":"57.10","LPIPS":"0.168","MMD":"0.112"},"uses_additional_data":false},{"leaderboard":"/sota/image-reconstruction-on-edge-to-shoes","task":"Image Reconstruction","dataset":"Edge-to-Shoes","model":"PI-REC","rank_in_archive_order":1,"of":4,"metrics":{"FID":"0.015","HP":"62.30","LPIPS":"0.085","MMD":"0.081"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}