Image Generation and Feedback Based on Deep Learning in Visual Communication Design

The feedback process plays an important role in visual communication design and has a profound impact on the effect of visual information transmission. This paper takes image generation as the main line, and focuses on the analysis of feedback behavior in two ways: real-time interaction and data synchronization acquisition. The study found that with the help of deep learning methods, it can effectively promote the expression of the relationship between the outputter and the viewpoint and the position in the graphical memory task, and improve the ability to map attributes such as images. This paper also conducts a functional test analysis of the platform, and the test results show that the processing time of the platform is between 2 seconds and 4 seconds. The flexible control of the time required for the feedback process is realized in the form of pictures, which further improves the effect of visual communication design. By learning a large amount of image data, these models can automatically identify and repair defects in images, such as blurring, noise, and missing parts. Research has found that deep learning based image restoration and enhancement techniques have made significant improvements in restoring image quality.