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Most existing methods do not consider various low-quality image conditions that exist in real-world situations. Low-quality image conditions mean that an image can have low resolution, plate imperfection effects, variable illumination environments or background objects similar to the license plate (LP). To improve the anti-interference ability and the speed performance of algorithm, this study aims to develop a parallel partial enhancement method based on color differences that demonstrates improved localization performance for blue\u2013white LP images under low-quality conditions. A novel color difference model is exploited to enhance LP areas and filter non-LP areas. Blue\u2013white color ratio and projection analysis are performed to select the exact LP area from the candidates. Moreover, this study develops a parallel version based on a multicore CPU for real-time processing for industrial applications. An image database including 395 low-quality car images captured from various scenes under different conditions is tested for the performance evaluation. The extensive experiments show the effectiveness and efficiency of the proposed approach. <\/jats:p>","DOI":"10.1142\/s0218126621503011","type":"journal-article","created":{"date-parts":[[2021,7,9]],"date-time":"2021-07-09T09:59:41Z","timestamp":1625824781000},"source":"Crossref","is-referenced-by-count":0,"title":["A Parallel Partial Enhancement Method for License Plate Localization in Low-Quality Images"],"prefix":"10.1142","volume":"30","author":[{"given":"Sainan","family":"Xiao","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, P. R. China"},{"name":"College of Information and Electronic Engineering, Hunan City University, Yiyang 413000, P. R. 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