Multiscale processing of image gradient domain-based convolutional neural networks for visual feature enhancement

In recent years, with the development of science and technology, image enhancement has become a very important topic in scienti ic research, become an indispensable part of machine vision, and has a wide range of applications in various ields of computer vision.In this paper, the image gradient enhancement algorithm is irst improved based on the image gradient ield, and its enhancement effect on low quality (low resolution) images is found to be poor through experiments.For this reason, the study constructs a multi-scale feature image enhancement model (LIEN-MFC) by convolutional neural network to further optimize the image enhancement effect.By comparing with different algorithms, the average PSNR of the model is 21.80 and the average SSIM is 0.8767, and it outperforms other compared algorithms in both PSNR and SSIM.In addition, the ablation experiments demonstrate that the enhancement effect of the LIEN-MFC model is further improved on the basis of the improved image gradient enhancement algorithm.The results show that the image enhancement model algorithm with multi-scale features proposed in this paper has a signi icant image enhancement effect and the improved image gradient enhancement in image enhancement of convolutional neural networks improves the model performance to some extent.