Recognition and Processing of Visual Communication Images based on Image Generation Algorithm

In the field of visual communication, the development of image recognition and processing technology is very important to improve the efficiency and accuracy of information transmission. Aiming at the problem that traditional image recognition methods are not accurate enough in complex scenes, this paper puts forward a visual communication image recognition and processing method based on GAN. This paper first describes the process of data collection and preprocessing, including obtaining image data from multiple channels, fine labeling, image cleaning, size adjustment, normalization and data enhancement. Then this paper designs an image generation algorithm based on GAN, optimizes the image quality through the confrontation training between generator and discriminator, and applies it to key links such as feature extraction, classification and detection. The data shows that the recognition accuracy of GAN in 10 image sets is significantly higher than that of SVM. In the first image set, the recognition accuracy of GAN is 90.9%, while that of SVM is only 69.4%. In addition, the average processing time of GAN is 866.1 milliseconds, which is lower than that of SVM, showing a faster processing speed. Based on the comprehensive experimental results, this study proves the superior performance of GAN in visual communication image recognition and processing, especially in terms of accuracy and processing efficiency, which provides new ideas and methods for the development of image recognition technology.