Blind Face Restoration Survey

Sait Sharipov, Bulat Nutfullin, Narek Maloyan

Abstract


The importance of researching methods for blind face restoration (BFR) arises from their potential practical applications in various domains. Examples of such areas include digital art and computer graphics for character face reconstruction and animation, as well as social networks and mobile applications, where they contribute to improving the quality of images and videos. In this paper, we conduct a review of contemporary methods and approaches used for solving the BFR problem. We examine various types of models based on generative adversarial networks, autoencoders, and diffusion models, which have demonstrated significant progress in this field. Specifically, we analyze key aspects such as network architecture, loss functions, quality metrics, and datasets. Furthermore, we discuss the issues and limitations of existing methods, as well as possible directions for future research. In particular, we emphasize the need for developing algorithms that are robust to various degradations and capable of adapting to different lighting conditions, poses, and facial expressions. In conclusion, we provide a systematic comparison of existing methods and summarize their merits and drawbacks.

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References


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