Aiming at the problem that images taken in low-light environments are affected by the strength of illumination， which leads to poor image quality， this study proposes a low-light image enhancement algorithm based on the fusion of bilateral filtering MSR and AutoMSRCR. First， the brightness of the original low-light image is enhanced using the MSR algorithm based on bilateral filtering in HSV color space. As a result， a brightness-enhanced image with the original color information is obtained. Then， the CLAHE algorithm is used to enhance the details of the brightness channel based on the Lab color space， and a detail-enhanced image is obtained. Finally， the AutoMSRCR algorithm is used to process the original low-light image and perform weighted fusion with the detail-enhanced image to obtain the final enhanced image. Using UCIQE， AG， SD， and IE as evaluation indexes， the proposed algorithm outperformed the MSR， MSRCR， CLAHE， and GAMMA algorithms. The results show that the proposed algorithm optimized image quality with UCIQE， AG， SD， and IE reaching values of 0.472 1， 12.674 2， 0.263 2， and 7.637 9， respectively. The obtained image contains more color information， is clearer， the image contrast is natural look， and the edge texture information of the image is more complete. That is， images enhanced by this algorithm are of the highest quality. This study provides a feasible method for low-light image enhancement.