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Graph."],"published-print":{"date-parts":[[2019,8,31]]},"abstract":"<jats:p>\n            Nearly every commodity imaging system we directly interact with, or indirectly rely on, leverages power efficient, application-adjustable black-box hardware image signal processing (ISPs) units, running either in dedicated hardware blocks, or as proprietary software modules on programmable hardware. The configuration parameters of these black-box ISPs often have complex interactions with the output image, and must be adjusted prior to deployment according to application-specific quality and performance metrics. Today, this search is commonly performed\n            <jats:italic>manually<\/jats:italic>\n            by \"golden eye\" experts or algorithm developers leveraging domain expertise. We present a\n            <jats:italic>fully automatic<\/jats:italic>\n            system to optimize the parameters of black-box hardware and software image processing pipelines according to any arbitrary (i.e., application-specific) metric. We leverage a\n            <jats:italic>differentiable<\/jats:italic>\n            mapping between the configuration space and evaluation metrics, parameterized by a convolutional neural network that we train in an end-to-end fashion with imaging hardware in-the-loop. Unlike prior art, our\n            <jats:italic>differentiable proxies<\/jats:italic>\n            allow for high-dimension parameter search with stochastic first-order optimizers, without explicitly modeling any lower-level image processing transformations. As such, we can efficiently optimize black-box image processing pipelines for a variety of imaging applications, reducing application-specific configuration times from months to hours. Our optimization method is fully automatic, even with black-box hardware in the loop. We validate our method on experimental data for real-time display applications, object detection, and extreme low-light imaging. The proposed approach outperforms manual search qualitatively and quantitatively for all domain-specific applications tested. When applied to traditional denoisers, we demonstrate that---just by changing hyperparameters---traditional algorithms can outperform recent deep learning methods by a substantial margin on recent benchmarks.\n          <\/jats:p>","DOI":"10.1145\/3306346.3322996","type":"journal-article","created":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T19:04:08Z","timestamp":1562958248000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":61,"title":["Hyperparameter optimization in black-box image processing using differentiable proxies"],"prefix":"10.1145","volume":"38","author":[{"given":"Ethan","family":"Tseng","sequence":"first","affiliation":[{"name":"Princeton University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Yu","sequence":"additional","affiliation":[{"name":"Princeton University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuting","family":"Yang","sequence":"additional","affiliation":[{"name":"Princeton University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahim","family":"Mannan","sequence":"additional","affiliation":[{"name":"Algolux, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karl ST.","family":"Arnaud","sequence":"additional","affiliation":[{"name":"Algolux, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derek","family":"Nowrouzezahrai","sequence":"additional","affiliation":[{"name":"McGill University, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Fran\u00e7ois","family":"Lalonde","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Laval, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Heide","sequence":"additional","affiliation":[{"name":"Princeton University and Algolux, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,7,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00182"},{"key":"e_1_2_1_2_1","volume-title":"A Connectionist Machine for Genetic Hillclimbing","author":"Ackley D.","unstructured":"D. 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