Optimization algorithm for low-light image enhancement based on Retinex theory

Abstract

To improve the visual quality of low-light images and discover hidden details in images, an image enhancement algorithm is proposed, which is based on a fast and robust fuzzy C-means (FRFCM) clustering algorithm combined with Retinex theory. The algorithm is based on Retinex theory to solve the above problems as followings: Firstly, the initial illumination estimation image is constructed by max-RGB and segmented by FRFCM algorithm. Secondly, initial illumination estimation image and its segmented image linearly fused with a certain proportion is to obtain the optimized illumination estimation image, then is smoothed by guided filtering. Finally, reflected image is obtained by Retinex theory and the edge details of the image by equal ratio are enhanced, showing the enhanced image rich in detail texture. In order to verify the proposed algorithm, a large number of low-light image datasets are applied to test the proposed algorithm. And the effects of image enhancement of the algorithm and other existing enhanced algorithms are also compared. The experimental results show that the proposed algorithm performs well in both subjective and objective evaluation, especially the good ability to keep meticulous of details and colour retention.