Low Illumination Color Image Enhancement Based on Improved Retinex Theory
Ping Wang, Zhiwen Wang, Dong Lv, Canlong Zhang, Yuhang Wang · Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering · 2020
In view of the defects of the classical Retinex image enhancement algorithm, such as poor texture detail retention, halo and over enhancement, and abrupt tone, this paper proposes a low illumination color image enhancement algorithm based on Gabor filter and retinex theory. Firstly, the brightness I component of the image is extracted in the HSI color space, and then the image with the brightness component is enhanced by MSRCR (the color-recovered retinex algorithm). At the same time, the image is enhanced by SSR (the single-scale retinex algorithm) based on Gabor filter in RGB color space, and the image with better texture and edge details is obtained. Finally, the final enhanced image is obtained by weighted fusion. The algorithm is compared with SSR, MSR (the multi-scale retinex algorithm) and MSRCR. The experimental results show that the image information processed by this algorithm is richer in color, hue and the color is closer to the original image, and it effectively reduces the occurrence of halo and over-enhancement. This algorithm can enhance the image of some low illumination color images, and the visual effect of the enhanced image is relatively peaceful.