In this paper, we propose an information enhancement method for surface disease images of ancient city walls based on adaptive correction of the illumination component to solve the problems of hidden information of surface disease images of ancient city walls and poor disease feature extraction and recognition due to the interference of sunlight change, occlusion between objects, and shooting angle deviation. The light component was extracted using continuous convolution in a multiscale Gaussian function, and the Gamma parameters were determined according to the nonlinear transformation relationship between the deviation of the light component and the mean value of the pixel. Next, an enhanced two-dimensional (2D)-Gamma function was constructed to adaptively correct the luminance values of the overbright and dark areas of the image to recover the hidden information. At the same time, homomorphic filtering was used to suppress the illumination components of the highlighted areas in the frequency domain in order to solve the problem of weakness of the 2D-Gamma function in characterizing the attenuation of the brightness values of highlighted pixels. This reduces the dynamic range of luminance and enhances the hidden information of highlighted areas of the image. A coefficient factor was used to linearly fuse the brightness-corrected image with the edge details extracted using the Sobel operator to reconstruct an enhanced surface disease image of the ancient city walls with clear details and prominent textures. The experimental results show that the proposed method effectively solves the problem of hidden image information and poor visibility caused by uneven illumination, occlusion, and shadows while retaining the original image details. Moreover, it enhances the surface disease image features of the ancient city walls and improves the accuracy of disease recognition.