Abstract
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 this https URL.
Abstract (translated)
零 artifact超分辨率（SR）旨在通过严格的原始内容完整性将低分辨率图像转换为高分辨率图像，消除任何扭曲或合成细节。尽管传统的扩散基 SR 技术在增强图像细节方面表现出惊人的能力，但在迭代过程中易引入伪影。这些伪影，从轻微噪声到不真实纹理，与源图像的真实结构不符，从而挑战了超分辨率过程的可靠性。在这项工作中，我们提出了自适应现实引导扩散（SARGD）方法，一种无需训练的方法，深入挖掘潜在空间以有效地识别和减轻伪影的传播。 SARGD 首先使用一个伪影检测器识别不合理的像素，创建一个二进制掩码突出显示伪影。接下来，现实引导精炼（RGR）过程通过将掩码与现实主义的潜在表示集成来优化伪影。然而，低质量图像的初始现实主义潜在表示在最终输出中导致过度平滑。为解决这个问题，我们引入了自适应引导（SAG）机制。它动态地计算现实分数，提高现实潜在的尖锐度。这些交替机制共同实现零 artifact 的超分辨率。 丰富的实验证明了我们方法的优势，在减少采样步骤的同时提供详细的无伪影高分辨率图像。您可以在以下链接处获取我们的代码：https://www.osac.tsinghua.edu.cn/group/Home/
URL
https://arxiv.org/abs/2403.16643
