Abstract
Diffusion models, known for their powerful generative capabilities, play a crucial role in addressing real-world super-resolution challenges. However, these models often focus on improving local textures while neglecting the impacts of global degradation, which can significantly reduce semantic fidelity and lead to inaccurate reconstructions and suboptimal super-resolution performance. To address this issue, we introduce a novel two-stage, degradation-aware framework that enhances the diffusion model's ability to recognize content and degradation in low-resolution images. In the first stage, we employ unsupervised contrastive learning to obtain representations of image degradations. In the second stage, we integrate a degradation-aware module into a simplified ControlNet, enabling flexible adaptation to various degradations based on the learned representations. Furthermore, we decompose the degradation-aware features into global semantics and local details branches, which are then injected into the diffusion denoising module to modulate the target generation. Our method effectively recovers semantically precise and photorealistic details, particularly under significant degradation conditions, demonstrating state-of-the-art performance across various benchmarks. Codes will be released at this https URL.
Abstract (translated)
扩散模型以其强大的生成能力而闻名，在解决现实世界的超分辨率挑战中发挥着关键作用。然而，这些模型通常只关注改善低分辨率图像的局部纹理，而忽视全局退化的影响，这可能导致语义保真度降低，从而导致不准确的重建和次优的超分辨率性能。为了解决这个问题，我们引入了一个新颖的两天平框架，该框架增强了扩散模型在低分辨率图像中识别内容和退化的能力。在第一阶段，我们采用无监督的对比学习来获得图像退化的表示。在第二阶段，我们将退化感知模块集成到简单的控制网络中，使得模型能够根据学习的表示对各种退化进行灵活的适应。此外，我们将退化感知的特征分解为全局语义和局部细节分支，然后注入到扩散去噪模块中，调节目标生成。我们的方法在显著的退化条件下有效地恢复了语义精确和逼真的细节，特别是在退化较大时，展示了在各种基准测试中的最先进性能。代码将在该https URL上发布。
URL
https://arxiv.org/abs/2404.00661
