Blind face restoration based on Swin Transformer and Style-Based Generator

Abstract:

Blind face restoration is the process of restoring a high-quality image from a low-quality image (e.g., blurred, noisy, or compressed image). Since the degradation type and degradation parameters of the low-quality image are unknown, blind face restoration is a highly ill-posed problem that heavily relies on various facial prior such as facial components and facial landmarks during the restoration process. However, these facial priors are typically extracted or estimated from low-quality images, which may be inaccurate, directly affecting the final restoration performance. The current mainstream methods mostly use ConNets for feature extraction and do not consider long-distance features, resulting in a lack of continuous consistency in the final results.The authors propose an improved StyleGAN model named SwinStyleGAN, which uses Swin Transformer to extract long-distance features and gradually generates images through an improved StyleGAN synthesis network.Addtionally, the authors design a Spatial Attention Transformation (SAT) module to reassign pixel weights of each stage feature to further constrain the generator. Experiments show that the proposed SwinStyleGAN in this paper has better blind face restoration performance.