Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey

This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. Th goal of this challenge is to upscale RAW Bayer images by 2x, considering unknown degradations such as noise and blur. In the challenge, a total of 230 participants registered, and 45 submitted results during thee challenge period. The performance of the top-5 submissions is reviewed and provided here as a gauge for the current state-of-the-art in RAW Image Super-Resolution.

Face2Face: Label-driven Facial Retouching Restoration

With the popularity of social media platforms such as Instagram and TikTok, and the widespread availability and convenience of retouching tools, an increasing number of individuals are utilizing these tools to beautify their facial photographs. This poses challenges for fields that place high demands on the authenticity of photographs, such as identity verification and social media. By altering facial images, users can easily create deceptive images, leading to the dissemination of false information. This may pose challenges to the reliability of identity verification systems and social media, and even lead to online fraud. To address this issue, some work has proposed makeup removal methods, but they still lack the ability to restore images involving geometric deformations caused by retouching. To tackle the problem of facial retouching restoration, we propose a framework, dubbed Face2Face, which consists of three components: a facial retouching detector, an image restoration model named FaceR, and a color correction module called Hierarchical Adaptive Instance Normalization (H-AdaIN). Firstly, the facial retouching detector predicts a retouching label containing three integers, indicating the retouching methods and their corresponding degrees. Then FaceR restores the retouched image based on the predicted retouching label. Finally, H-AdaIN is applied to address the issue of color shift arising from diffusion models. Extensive experiments demonstrate the effectiveness of our framework and each module.