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Then, through a illumination compensation strategy, the illumination component is processed and combined with the reflectance component to synthesize the enhanced value component (V). Finally, an adaptive global balance strategy is applied to optimize the enhanced value component (V) to ensure that the resulting image appears more natural and conforms to human visual perception habits. Experimental results demonstrate the effectiveness and superiority of our method compared to existing traditional processing algorithms and deep learning methods, showing excellent performance in enhancing dark details of images and maintaining natural colors.<\/jats:p>","DOI":"10.1142\/s0218194025410025","type":"journal-article","created":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:33:25Z","timestamp":1742603605000},"page":"1555-1571","source":"Crossref","is-referenced-by-count":1,"title":["Low-Light Image Enhancement Based on Retinex Reflectance Compensation"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1752-5634","authenticated-orcid":false,"given":"Zhaozheng","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Saihan District, Hohhot 010021, P. R. 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