The Retinex enhancement algorithm for low-light intensity image based on improved illumination map

Taken in low-light intensity conditions, image with low brightness affects processing precision. In this article, the Gamma Function based on the brightness average and weighted fusion method according to gray entropy is proposed, which is combined with the improved Retinex algorithm. First, the maximum values of R, G, and B channels in original image are extracted to generate the primary illumination map. Second, the illumination map is optimized and adjusted via the Gamma correction function based on the average brightness value. Finally, the illumination map and detail layer are fused by a weighted fusion algorithm of gray entropy to obtain the reflection map. Reflection maps are used as enhancement. The algorithm proposed in this article can improve the brightness and maintain light distribution in the original image with higher precision and less color distortion. We propose a gamma function based on the brightness average. We propose a weighted fusion method according to the gray entropy. Based on the Retinex framework, we have adapted the method of acquiring and optimizing the illumination map. image