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Used by artists, photographers, system engineers, and for downstream vision tasks, traditional image processing pipelines feature complex algorithmic branches developed over decades. Recently, image-to-image networks have made great strides in image processing, style transfer, and semantic understanding. The differentiable nature of these networks allows them to fit a large corpus of data; however, they do not allow for intuitive, fine-grained controls that photographers find in modern photo-finishing tools.<\/jats:p>\n          <jats:p>This work closes that gap and presents an approach to making complex photo-finishing pipelines differentiable, allowing legacy algorithms to be trained akin to neural networks using first-order optimization methods. By concatenating tailored network proxy models of individual processing steps (e.g. white-balance, tone-mapping, color tuning), we can model a non-differentiable reference image finishing pipeline more faithfully than existing proxy image-to-image network models. We validate the method for several diverse applications, including photo and video style transfer, slider regression for commercial camera ISPs, photography-driven neural demosaicking, and adversarial photo-editing.<\/jats:p>","DOI":"10.1145\/3550454.3555526","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T21:19:07Z","timestamp":1669843147000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Neural Photo-Finishing"],"prefix":"10.1145","volume":"41","author":[{"given":"Ethan","family":"Tseng","sequence":"first","affiliation":[{"name":"Princeton University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Princeton University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lars","family":"Jebe","sequence":"additional","affiliation":[{"name":"Adobe"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuaner","family":"Zhang","sequence":"additional","affiliation":[{"name":"Adobe"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihao","family":"Xia","sequence":"additional","affiliation":[{"name":"Adobe"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifei","family":"Fan","sequence":"additional","affiliation":[{"name":"Adobe"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felix","family":"Heide","sequence":"additional","affiliation":[{"name":"Princeton University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawen","family":"Chen","sequence":"additional","affiliation":[{"name":"Adobe"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00453"},{"key":"e_1_2_1_2_1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1692--1700","author":"Abdelhamed Abdelrahman","unstructured":"Abdelrahman Abdelhamed, Stephen Lin, and Michael S. 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