Low illumination color image enhancement based on Gabor filtering and Retinex theory

Abstract

Aiming at the shortcomings of traditional Retinex image enhancement algorithms, such as poor texture detail retention, halo, over-enhancement and hue mutation, a low-illuminance color image enhancement algorithm based on Gabor filter and Retinex theory is proposed. The algorithm extracts the luminance I component from the HSI color space of the original image, and then performs MSRCR (Retinex algorithm for color restoration) enhancement on the luminance I component to obtain an enhanced luminance I component and color reproduction image. On the other hand, the original image is enhanced by a SSR (Single Scale Retinex Algorithm) based on the Gabor filter in the RGB color space to obtain an enhanced image with better texture and edge details. Then, the two images enhanced in two different ways are weighted and merged to obtain the final enhanced image. This algorithm is compared with the SSR algorithm based on Gamma correction, the MSR (multi-scale Retinex algorithm) based on bilateral filtering and the improved MSRCR algorithm. Taking mean square error, information entropy, and average gradient as evaluation indicators, the experimental results show that the image information processed by this algorithm is rich in color, rich in color, and color is closer to the original image, effectively reducing the occurrence of halo and excessive enhancement. This algorithm has certain reference significance for the enhancement of low-light color images.