An image contrast and brightness enhancement algorithm for human upper gastrointestinal endoscopy is proposed to address the problem of blurring of details such as insufficient and uneven illumination in endoscopic images. The algorithm improves and weighted fusion of the adaptive gamma-corrected luminance enhancement algorithm and contrast-limited adaptive histogram equalization algorithm. The input images are processed separately and the final weighted fused enhanced image is obtained. The proposed algorithm is applied to the partial images of the upper gastrointestinal tract in the open access dataset and compared with the existing algorithms for algorithm effect testing experiments, using peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and natural image quality evaluator (NIQE) as the image evaluation metrics. The experimental results show that the proposed algorithm enhances the image with higher quality than other algorithms, which significantly improves the image quality and provides a good basis for image detection.