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Considering the noise characteristics of complex illumination images, in this study, we propose a novel restoration algorithm for noisy complex illumination, which combines guided adaptive multi\u2010scale Retinex (GAMSR) and improvement BayesShrink threshold filtering (IBTF) based on double\u2010density dual\u2010tree complex wavelet transform (DDDTCWT) domain. Extensive restoration experiments are conducted on three typical types images and the same image with different noises. On the basis of a series of evaluation indexes, we compare our method to those of state\u2010of\u2010the\u2010art algorithms. The results show that (i) SSIM of the proposed IBTF is superior to traditional Bayes threshold method by 15% as the standard variance is 100. (ii) PSNR of the proposed GAMSR enhances 15% to traditional MSR. (iii) The clarity of final results for restoration speeds up three times than that of original images, and the information entropy is improved slightly too. Therefore, the proposed method can effectively enhance the details, edges and textures of the image under complex illumination and noises.<\/jats:p>","DOI":"10.1049\/iet-cvi.2018.5163","type":"journal-article","created":{"date-parts":[[2018,8,11]],"date-time":"2018-08-11T02:21:54Z","timestamp":1533954114000},"page":"224-232","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Restoration algorithm for noisy complex illumination"],"prefix":"10.1049","volume":"13","author":[{"given":"Zhanwen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Engineering Chang'an University Xi'an 710064 Shaanxi People's Republic of China"},{"name":"Institute of Transportation Studies University of California, Berkeley City of Berkeley State of California CA 94804\u20104648 USA"}]},{"given":"Tao","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Engineering Chang'an University Xi'an 710064 Shaanxi People's Republic of China"}]},{"given":"Fanjie","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Information Engineering Chang'an University Xi'an 710064 Shaanxi People's Republic of China"}]},{"given":"Ziheng","family":"Jiao","sequence":"additional","affiliation":[{"name":"School of Information Engineering Chang'an University Xi'an 710064 Shaanxi People's Republic of China"}]},{"given":"Aodong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Engineering Chang'an University Xi'an 710064 Shaanxi People's Republic of China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3994-2874","authenticated-orcid":false,"given":"Shuying","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation Xi'an University of Posts & Telecommunications Xi'an 710121 Shaanxi People's Republic of China"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering Auburn University Auburn State of Alabama, AL 36849 USA"}]}],"member":"265","published-online":{"date-parts":[[2018,9,7]]},"reference":[{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.08.015"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11045-017-0482-z"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2726546"},{"issue":"99","key":"e_1_2_9_5_2","first-page":"1","article-title":"Adaptive residual networks for high\u2010quality image restoration","author":"Zhang Y.","year":"2018","journal-title":"IEEE Trans. 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