A Naturalness‐Preserved Low‐Light Enhancement Algorithm for Intelligent Analysis

Abstract

Images/videos captured in low-light conditions often present low luminance and contrast. Although the existing low-light enhancement algorithms can improve the subjective perception, color distortion and over-enhancement are extremely obvious, which will disturb the subsequent intelligent analysis. Therefore, a naturalness-preserved low-light enhancement algorithm for intelligent analysis is proposed in this paper. An enhancement model is established in RGB color space. Images of ColorChecker color chart are captured under a series of light conditions. To preserve the naturalness, the factors of the proposed enhancement model are estimated by the images captured in practical illumination environment. Experimental results demonstrate that the proposed algorithm can produce natural enhanced results and improve the performance of vehicle license plate localization and skin color detection compared to the existing algorithms. Furthermore, the proposed algorithm can process the 720p videos at the speed of 28.3 fps on average.