让扩散模型学会修复自身生成的错误，提升图像恢复质量。
Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration
- 训练时引入带累积误差的图像，使模型学习纠正错误
- 在5项图像恢复任务中显著优于现有方法，减少形状与色彩失真
- 适合需要高保真度图像恢复的科研与工业场景
本文针对去噪扩散模型（DDMs）在图像恢复任务中常见的形状和颜色失真问题，提出一种名为数据一致性训练的新方法。我们发现这些失真源于训练与测试数据间的不一致。为此，该方法在训练过程中引入带有累积误差的图像，使模型学会纠正此类误差。实验结果表明，在五种图像恢复任务中，该方法显著优于当前最优方法，有效抑制失真并保持图像保真度。
原文摘要 · Abstract (English)
In this work, we address the limitations of denoising diffusion models (DDMs) in image restoration tasks, particularly the shape and color distortions that can compromise image quality. While DDMs have demonstrated a promising performance in many applications such as text-to-image synthesis, their effectiveness in image restoration is often hindered by shape and color distortions. We observe that these issues arise from inconsistencies between the training and testing data used by DDMs. Based on our observation, we propose a novel training method, named data-consistent training, which allows the DDMs to access images with accumulated errors during training, thereby ensuring the model to learn to correct these errors. Experimental results show that, across five image restoration tasks, our method has significant improvements over state-of-the-art methods while effectively minimizing distortions and preserving image fidelity.
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