This paper addresses the issue of hazy stray light in fundus retinal images, which leads to unclear blood vessel details. The proposed dehazing algorithm for fundus retinal images is based on the dark channel theory and incorporates Gamma transformation. The algorithm enhances the clarity of the image while preserving blood vessel information. This algorithm aims to defog images by processing the R, G and B channels separately. Firstly, the algorithm calculates the dark channel image using adaptive window minimum filtering and takes the average value of the top 0.1% pixels as the atmospheric illumination intensity value. Secondly, the algorithm solves the rough transmittance of the image and improves it using the guided filtering algorithm. Finally, the algorithm restores the haze-free image using the atmospheric scattering model and applies Gamma transformation. The experimental results show that the information entropy and average gradient of the restored image increase by an average of about 6.8% and 11.6%, respectively. The algorithm in this paper can quickly and effectively remove hazy stray light in the fundus retinal image, restore the image to be clear and natural, and retain the details information of retinal blood vessels.