During the camera imaging process, a gradual halo effect may occur due to changes in the viewing angle, resulting in a phenomenon of bright in the middle and dark around the image. The presence of gradual halo results in the loss of some edge texture information in the image, greatly affecting the performance of machine vision processing. To address this issue, this article aims to improve the Retinex-Net network model by correcting image clarity and improving denoising performance. Firstly, in order to maintain the high resolution of the corrected image while improving the receptive field, this paper adds dilated convolution on the basis of the original network model. Secondly, the algorithm improves the denoising method to a dense residual network denoising method, with the aim of densely extracting each layer's features of the vignetting image, preserving more of the image's detailed characteristics and suppressing noise. Finally, this article constructs a dataset of vignetting images and verifies the correction performance of the proposed vignetting correction algorithm on the test set. Compared with the original network model before improvement, the algorithm in this paper improves by 0.293 in SSIM value, 0.727 in PSNR value, and 0.095 in RMSE value. Compared with correction algorithms such as minimizing image entropy, adaptive compensation Retinex, and radial gradient symmetry, the algorithm in this paper has better correction performance and is more suitable for observation and understanding visually.