Research on Image Restoration Algorithms Based on Transformer Prediction Networks

In recent years, with the rapid development of computer vision, there have been significant advancements in image restoration techniques. However, many current algorithms still struggle with accurately restoring degraded images with lost information. In this paper, we present a new image restoration algorithm called CodeFormerGAN, which is based on the Transformer model. This algorithm consists of three main components: Codebook, CLT network, and CFT. The Codebook module is responsible for learning self-reconstruction and storing high-quality face image parts. The Transformer in the CLT network models the overall composition of the face using low-quality input. The CFT module transforms image features to achieve a flexible balance between restoration quality and fidelity. Additionally, we introduce a GAN-based image super-resolution enhancement algorithm to enhance the sharpness of the restored image. The results of our experiments demonstrate that our proposed method produces higher-quality restoration results and exhibits better robustness when processing images in complex scenes.