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）。尽管这些模型在近年展现出显著的性能提升，但本研究对其在图像修复任务中的适用性提出质疑。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突出。然而，在以提高图像感知质量为目标的图像修复中，这些模型难以通过低层表征建模退化图像与真实图像之间的关系。为验证这一观点，我们通过大量实验将最先进的CLDMs与传统图像修复模型进行对比。结果表明，尽管CLDMs具有规模化优势，但其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，传统方法的表现优于CLDMs。此外，我们通过实证研究探讨了不同CLDM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，为该领域开拓更多可能性。