Color Restoration Method of Art Images Based on Perceptual Network and Oriented to Serial Data Application in Wireless Systems

Due to the influence of camera Angle, camera exposure parameters and other reasons, the color and brightness of art painting images are significantly different. Therefore, this paper proposes a color correction method for art painting images based on perceptual network for the application of sequence data in wireless systems. When the damaged area is large or the semantic information is missing, the restoration effect will be greatly reduced. Therefore, a high-resolution generative network is designed as the back-end of the network to improve the resolution of the density map and further improve the model counting accuracy. Then, the multi-scale feature extraction network based on the perception network and the high-resolution density map generation network are connected and fused to build the dense crowd counting network based on the multi-scale perception network. The convolutional network using neural art images greatly reduces the feature quantity in the grid extraction of feature combination. This ensures the accuracy of multi-scale artistic images.