Objective This study aims to solve problems such as low contrast unclear edges and unremarkable details in medical images improve the visual effect of medical images and preserve good edge information. Methods A medical image enhancement algorithm based on improved multi-scale Retinex and Non-subsampled Shearlet Transform NSST is proposed. First the method improved the multi-scale Retinex by employing gradient-guided filtering which incorporated local image gradient information to steer the filtering process. According to the principle of multi-scale Retinex a reflected light component was calculated and an enhanced image was generated. Meanwhile the Non-subsampled Shearlet Transform NSST was introduced to decompose the image into high-frequency and low-frequency components. The low-frequency components were processed by gradient-guided filtering to extract the structural and characteristic information of the image and improve the background contrast. The image was restored through the inverse transform of NSST. The two images were fused using the Laplacian-Gaussian pyramid to enhance the common features and avoid over-exposure. Finally the Contrast Limited Adaptive Histogram Equalization CLAHE was used to improve the local contrast and obtain the final enhanced image. Results The results of simulation experiments and ablation experiments showed that this algorithm could effectively improve the contrast of medical images and completely preserve the details and edge information. Conclusion Through the objective analysis of four image evaluation indicators namely the peak signal-to-noise ratio PSNR entropy perceptual image quality evaluator PIQE and mean M it is found that this algorithm is optimal can effectively improve the quality of medical images and provide technical support for diagnosis.