用潜在扩散模型做图像修复的正则化，效果好还省计算。
RELD: Regularization by Latent Diffusion Models for Image Restoration
- 将训练好的去噪扩散模型嵌入变分框架，实现正则化。
- 在去噪、去模糊、超分辨率任务中表现优异，尤其感知质量高。
- 适合追求高质量图像修复且资源有限的研究者或工程师。
近年来，扩散模型已成为深度生成建模的新范式，取代了生成对抗网络的长期主导地位。受去噪正则化原理启发，本文提出一种将潜在扩散模型（训练用于去噪任务）融入基于半二次分裂的变分框架的方法，利用其正则化特性。该方法在多种成像应用中条件易于满足，可降低计算成本同时保持高质量结果。所提策略称为潜在去噪正则化（RELD），在自然图像数据集上对去噪、去模糊和超分辨率任务进行了测试。数值实验表明，RELD在各类指标下均具备竞争力，特别是在感知质量指标上表现突出。
原文摘要 · Abstract (English)
In recent years, Diffusion Models have become the new state-of-the-art in deep generative modeling, ending the long-time dominance of Generative Adversarial Networks. Inspired by the Regularization by Denoising principle, we introduce an approach that integrates a Latent Diffusion Model, trained for the denoising task, into a variational framework using Half-Quadratic Splitting, exploiting its regularization properties. This approach, under appropriate conditions that can be easily met in various imaging applications, allows for reduced computational cost while achieving high-quality results. The proposed strategy, called Regularization by Latent Denoising (RELD), is then tested on a dataset of natural images, for image denoising, deblurring, and super-resolution tasks. The numerical experiments show that RELD is competitive with other state-of-the-art methods, particularly achieving remarkable results when evaluated using perceptual quality metrics.
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