Self-Adaptive Reality-Guided Diffusion for Artifact-Free Super-Resolution

Qingping Zheng, Ling Zheng, Yuanfan Guo, Ying Li, Songcen Xu, Jiankang Deng, Hang Xu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 25806-25816

Artifact-free super-resolution (SR) aims to translate low-resolution images into their high-resolution counterparts with a strict integrity of the original content eliminating any distortions or synthetic details. While traditional diffusion-based SR techniques have demonstrated remarkable abilities to enhance image detail they are prone to artifact introduction during iterative procedures. Such artifacts ranging from trivial noise to unauthentic textures deviate from the true structure of the source image thus challenging the integrity of the super-resolution process. In this work we propose Self-Adaptive Reality-Guided Diffusion (SARGD) a training-free method that delves into the latent space to effectively identify and mitigate the propagation of artifacts. Our SARGD begins by using an artifact detector to identify implausible pixels creating a binary mask that highlights artifacts. Following this the Reality Guidance Refinement (RGR) process refines artifacts by integrating this mask with realistic latent representations improving alignment with the original image. Nonetheless initial realistic-latent representations from lower-quality images result in over-smoothing in the final output. To address this we introduce a Self-Adaptive Guidance (SAG) mechanism. It dynamically computes a reality score enhancing the sharpness of the realistic latent. These alternating mechanisms collectively achieve artifact-free super-resolution. Extensive experiments demonstrate the superiority of our method delivering detailed artifact-free high-resolution images while reducing sampling steps by 2X. We release our code at https://github.com/ProAirVerse/Self-Adaptive-Guidance-Diffusion.git.