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And the proposed discriminator is the PatchGAN with color-aware attention module, which enhances its ability to discriminate between true and false colorized images. Meanwhile, this paper proposes a novel composite loss function that can improve the quality of colorized images, generate fine local details, and recover semantic and texture information. Extensive experiments demonstrate that the proposed E2GAN has significantly improved SSIM, PSNR, LPIPS, and NIQE on the KAIST dataset and the FLIR dataset compared to existing methods.<\/jats:p>","DOI":"10.1007\/s40747-023-01079-3","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T02:01:21Z","timestamp":1686621681000},"page":"7015-7036","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Exploring efficient and effective generative adversarial network for thermal infrared image colorization"],"prefix":"10.1007","volume":"9","author":[{"given":"Yu","family":"Chen","sequence":"first","affiliation":[]},{"given":"Weida","family":"Zhan","sequence":"additional","affiliation":[]},{"given":"Yichun","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Depeng","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Xiaoyu","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Renzhong","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,13]]},"reference":[{"issue":"12","key":"1079_CR1","doi-asserted-by":"publisher","first-page":"23194","DOI":"10.1109\/TITS.2022.3194931","volume":"23","author":"J Chen","year":"2022","unstructured":"Chen J, Liu Z, Jin D, Wang Y, Yang F, Bai X (2022) Light transport induced domain adaptation for semantic segmentation in thermal infrared urban scenes. 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