Recent advancements in image restoration increasingly employ conditional latent diffusion models (CLDMs). While these models have demonstrated notable performance improvements in recent years, this work questions their suitability for IR tasks. CLDMs excel in capturing high-level semantic correlations, making them effective for tasks like text-to-image generation with spatial conditioning. However, in IR, where the goal is to enhance image perceptual quality, these models face difficulty of modeling the relationship between degraded images and ground truth images using a low-level representation. To support our claims, we compare state-of-the-art CLDMs with traditional image restoration models through extensive experiments. Results reveal that despite the scaling advantages of CLDMs, they suffer from high distortion and semantic deviation, especially in cases with minimal degradation, where traditional methods outperform them. Additionally, we perform empirical studies to examine the impact of various CLDM design elements on their restoration performance. We hope this finding inspires a reexamination of current CLDM-based IR solutions, opening up more opportunities in this field.

翻译：近年来，图像修复领域越来越多地采用条件隐扩散模型（CLDMs）。尽管这些模型在过去几年中展现出显著的性能提升，但本研究质疑其是否适用于图像修复任务。CLDMs擅长捕捉高层语义关联，使其在具有空间条件的文本到图像生成等任务中表现优异。然而，在图像修复中，目标在于提升图像的感知质量，这些模型难以利用低层表示来建模退化图像与真实图像之间的关系。为支持这一观点，我们通过大量实验比较了最先进的CLDMs与传统图像修复模型。结果表明，尽管CLDMs具有规模扩展优势，但它们存在高失真和语义偏差问题，尤其在退化程度较低的情况下，传统方法表现更优。此外，我们通过实证研究探讨了CLDM不同设计要素对其修复性能的影响。我们希望这一发现能促使学界重新审视当前基于CLDM的图像修复方案，为该领域开辟更多机遇。