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.

翻译：无伪影超分辨率旨在将低分辨率图像转换为高分辨率图像，同时严格保持原始内容的完整性，消除任何失真或合成细节。尽管传统的基于扩散的超分辨率技术在增强图像细节方面展现出显著能力，但其迭代过程容易引入伪影。这些伪影从微小的噪声到不真实的纹理不等，偏离了源图像的真实结构，从而挑战了超分辨率过程的完整性。在这项工作中，我们提出了自适应现实引导扩散（SARGD），一种无需训练的方法，它深入潜空间以有效识别并抑制伪影的传播。我们的SARGD首先使用伪影检测器识别不合理的像素，生成一个高亮伪影的二进制掩码。随后，现实引导精炼（RGR）过程通过将该掩码与真实潜表示相结合来优化伪影，从而改善与原始图像的对齐。然而，来自较低质量图像的初始真实潜表示会导致最终输出过度平滑。为解决此问题，我们引入了自适应引导（SAG）机制。它动态计算现实分数，增强真实潜表示的锐度。这些交替机制共同实现了无伪影的超分辨率。大量实验证明了我们方法的优越性，在将采样步骤减少2倍的同时，生成了细节丰富的无伪影高分辨率图像。我们在https://github.com/ProAirVerse/Self-Adaptive-Guidance-Diffusion.git发布代码。