Self-Adaptive Reality-Guided Diffusion for Artifact-Free
  Super-Resolution
Qingping Zheng Ling Zheng Yuanfan Guo Ying Li Songcen Xu Jiankang Deng Hang Xu
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.