Soft binary segmentation-based backlit image enhancement

Zhenhao Li, Kai Cheng, Xiaolin Wu

Abstract: This paper is concerned with the enhancement of backlit images by compensating for abnormal illumination conditions. The underexposed (backlit) or/and overexposed regions in a backlit image are identified by a soft binary segmentation process that is driven by a Gaussian mixture model. Optimal tone-mapping is performed on the under- and over-exposed regions separately to improve the visual quality. Experimental results demonstrate the efficacy of the proposed restoration method and its advantages over existing image enhancement algorithms in perceptual quality.