{"id":122905,"date":"2022-02-15T23:27:26","date_gmt":"2022-02-15T14:27:26","guid":{"rendered":"http:\/\/175.125.95.178\/ai-in-signal\/22905\/"},"modified":"2026-04-07T10:48:21","modified_gmt":"2026-04-07T01:48:21","slug":"22905","status":"publish","type":"ai-in-signal","link":"http:\/\/ee.presscat.kr\/en\/ai-in-signal\/22905\/","title":{"rendered":"Learning Color Representations for Low-Light Image Enhancement Bomi Kim, Sunhyeok Lee, Nahyun Kim, Donggon Jang (WACV 2022)"},"content":{"rendered":"<p style=\"text-align:justify;margin-bottom:11px\">\n<div class=\"\"><img decoding=\"async\" class=\"\" src=\"\/wp-content\/uploads\/drupal\/\uae40\ub300\uc2dd3.png\" alt=\"\" title=\"\"><\/div>\n<\/p>\n<p style=\"text-align:justify;margin-bottom:11px\">&nbsp;<\/p>\n<p style=\"text-align:justify;margin-bottom:11px\">\n<div class=\"\"><img decoding=\"async\" class=\"\" src=\"\/wp-content\/uploads\/drupal\/\uae40\ub300\uc2dd5.png\" alt=\"\" title=\"\"><\/div>\n<\/p>\n<p style=\"text-align:justify;margin-bottom:11px\">&nbsp;<\/p>\n<p style=\"text-align:justify;margin-bottom:11px\"><span style=\"font-size:10pt\"><span style=\"line-height:107%\"><span>Color conveys important information about the visible world. However, under low-light conditions, both pixel intensity, as well as true color distribution, can be significantly shifted. Moreover, most of such distortions are non-recoverable due to inverse problems. In the present study, we utilized recent advancements in learning-based methods for low-light image enhancement. However, while most &#8220;deep learning&#8221; methods aim to restore high-level and object-oriented visual information, we hypothesized that learning-based methods can also be used for restoring color-based information. To address this question, we propose a novel color representation learning method for low-light image enhancement. More specifically, we used a channel-aware residual network and a differentiable intensity histogram to capture color features. Experimental results using synthetic and natural datasets suggest that the proposed learning scheme achieves state-of-the-art performance. We conclude from our study that inter-channel dependency and color distribution matching are crucial factors for learning color representations under low-light conditions.<\/span><\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>1017<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-122905","ai-in-signal","type-ai-in-signal","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-signal\/122905","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-signal"}],"about":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/types\/ai-in-signal"}],"wp:attachment":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/media?parent=122905"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}