Image process is widely used in route planning, industrial damage detection, face recognition, medical aided diagnosis and other fields, and the vigorous development of this technology also has higher requirements for the performance of image acquisition equipment. The insufficient exposure and inconspicuous texture of the detected object in the image will be affected by the general deviation of the image quality collected by the low-end equipment, while the high-end image acquisition equipment is generally expensive and has a precise structure, which is not suitable for use in harsh acquisition environments. At the same time, the distance between the position where the detected object appears and the image sensor is also affected by uncontrollable factors such as randomness. These bad factors all increase the difficulty of the later target recognition task, and also bring a bad visual experience to the user. Therefore, it is of great theoretical significance and application value to design an enhancement algorithm to improve the quality of low-light images.To solve the problems of low contrast and blurred details in low-illumination images, a multi-domain block low-illumination image enhancement algorithm fused with genetic algorithm is proposed. The algorithm can be divided into four stages: color space conversion, brightness enhancement, detail enhancement and multi-scale fusion. First of all, to prevent the original color characteristics of the image from changing when the image brightness is enhanced, the input image is converted from the RGB color space to the HSV color space, so as to better separate the color information and brightness information of the input image, so that the color of the image can be improved. Information is not altered when augmented. When the algorithm enhances the image brightness, in order to prevent the over-exposure or under-exposure of some areas of the processed image, it is necessary to reduce the impact of the complex exposure of the acquisition scene on the enhanced image brightness. Therefore, the multi-threshold block enhancement is more in line with the actual scene. The method is used to enhance the brightness of the image. In order to improve the processing speed of the algorithm, the genetic algorithm is used to search for the optimal segmentation threshold of the brightness component of the input image. Then, the luminance channel of the input image is divided into a plurality of different exposure level sub-images according to the obtained multiple thresholds and the image luminance gradient law. The detailed information contained in each sub-image is the evaluation criterion, and the complexity of all sub-images is evaluated through a multi-threshold block enhancement algorithm. The brightness of each sub-image is adjusted according to the evaluation results, and the arrangement order of the brightness of each sub-image after enhancement same as that before enhancement. To enhance the detail information of the image, the guided filtering algorithm is introduced, and the original image is subjected to two guided filtering processes. The first process filters out image noise, and the second filter enhances the contour information of the image. After two filtering processes, the enhancement results with rich contour information and less noise are obtained. The unsharp mask algorithm is introduced, which uses low-pass filtering to obtain the low-frequency information of the original image, subtracts the original image and the low-frequency information to obtain the high-frequency information of the image, and superimposes the enhanced high-frequency information with the original image to obtain the high-frequency information of the image. Finally, a multi-scale fusion algorithm is introduced to decompose the enhanced input image contour information, enhanced input image texture information details and brightness enhanced input image into Laplacian pyramid and Gaussian pyramid. The resulting Laplacian input and the corresponding Gaussian weight map at each level obtain the final enhancement result from texture information, contour information and brightness information.The algorithm is also compared with the existing enhanced algorithms. The results show that the increase of each index of the image enhanced by the proposed algorithm is greater than that of other comparison algorithms, and the proposed algorithm effectively solves the problem of color distortion and brightness blocking while enhancing image brightness, effectively restoring the texture information of the image. At the same time, the brightness distribution of the enhanced image restores the brightness of the real shooting environment. It proves that the algorithm has better performance.