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As a representative scenario, environmental temperature variation may degrade image quality due to thermal defocus and sensor response, resulting in blur and noise. However, extensive athermalization in optics usually requires a complex design process and is limited by materials. Herein, a multibranch computational imaging scheme is developed, using emerging generative adversarial networks as the postprocessing to compensate for degradation of all kinds caused by thermal defocus and noise. In addition, a temperature controllable data acquisition, division, and mixture scheme is described to facilitate effective datasets for model robustness. Experiments on a vehicle lens and a mobile phone lens reveal that the proposed multibranch learned strategy notably increases image quality in the temperature range of 0\u201380\u2009\u00b0C, and outperforms conventional athermalization in most instances, which is beneficial to lowering the design and manufacturing costs of imaging systems.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202200149","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T23:49:37Z","timestamp":1663976977000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Temperature\u2010Robust Learned Image Recovery for Shallow\u2010Designed Imaging Systems"],"prefix":"10.1002","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4960-2398","authenticated-orcid":false,"given":"Wei","family":"Chen","sequence":"first","affiliation":[{"name":"College of Optical Science and Engineering Zhejiang University  Hangzhou 310027 China"}]},{"given":"Bingyun","family":"Qi","sequence":"additional","affiliation":[{"name":"College of Optical Science and Engineering Zhejiang University  Hangzhou 310027 China"}]},{"given":"Xu","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Optical Science and Engineering Zhejiang University  Hangzhou 310027 China"}]},{"given":"Haifeng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Optical Science and Engineering Zhejiang University  Hangzhou 310027 China"},{"name":"Research Center for Intelligent Chips and Devices Zhejiang Lab  Hangzhou 311121 China"}]},{"given":"Xiang","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Optical Science and Engineering Zhejiang University  Hangzhou 310027 China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0667-2599","authenticated-orcid":false,"given":"Yifan","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering The University of Hong Kong  Hong Kong Hong Kong"}]}],"member":"311","published-online":{"date-parts":[[2022,9,23]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2015.12.005"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/JEDS.2014.2306412"},{"key":"e_1_2_9_4_1","first-page":"5","volume":"40","author":"Chen S.","year":"2021","journal-title":"ACM Trans. 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