动态时间步预测在类模糊图像恢复任务的逆热扩散中的应用

中文摘要 AI 辅助

在使用扩散模型处理图像恢复问题时，通常采用扩散反演来保留退化图像中的相关信息。许多方法不是反演到初始时间步（即T），而是反演到预先确定的中间时间步，以便更好地保留退化源图像的信息。然而，预先确定的反演时间步对于重建并不理想，因为严重退化的图像需要比轻度退化的图像更早的起始时间步。此外，基于DDIM的模型通过添加高斯噪声来破坏原始信号，这可能与模糊、雾霾和低光等类模糊退化的性质不匹配。为了解决这些问题，我们提出了两个解决方案：（1）采用一种替代的扩散过程，称为逆热扩散模型，该模型通过逐渐模糊数据点来扩散输入图像；（2）提出实现一个时间预测器来估计反演的起始时间步，使模型学习适应退化严重程度。在标准基准上的大量实验表明，我们的方法在定量和定性评估中均达到了最先进的性能，并对许多恢复任务具有出色的泛化能力。

英文摘要

When using diffusion models to target image restoration problems, diffusion inversion is typically employed to retain relevant image information from the degraded images. Instead of inverting back to the initial time step (i.e., T), many methods invert to a pre-determined intermediate time step, in order to better preserve information from degraded source images. However, a pre-determined time step for inversion is not ideal for reconstruction, as a severely degraded image requires an earlier starting time step than a mildly degraded one. In addition, DDIM-based models corrupt the original signal by adding Gaussian noise, which can be mismatched to the nature of blur-like degradations, such as blur, haze, and low-light. To address these problems, we propose two solutions: (1) we adopt an alternative diffusion process, called the Inverse Heat Dissipation Model, that diffuses the input image by gradually blurring a data point (2) we propose to implement a time predictor to estimate the starting time step for the inversion, with the model learning to adapt to the degradation severity. Extensive experiments on standard benchmarks show that our method achieves state-of-the-art performance in both quantitative and qualitative evaluations, with excellent generalization to many restoration tasks.

发表机构

• Nanyang Technological University（南洋理工大学）

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