Abstract
Denoising diffusion models have achieved state-of-the-art performance in image restoration by modeling the process as sequential denoising steps. However, most approaches assume independent and identically distributed (i.i.d.) Gaussian noise, while real-world sensors often exhibit spatially correlated noise due to readout mechanisms, limiting their practical effectiveness. We introduce Correlation Aware Restoration with Diffusion (CARD), a training-free extension of DDRM that explicitly handles correlated Gaussian noise. CARD first whitens the noisy observation, which converts the noise into an i.i.d. form. Then, the diffusion restoration steps are replaced with noise-whitened updates, which inherits DDRM's closed-form sampling efficiency while now being able to handle correlated noise. To emphasize the importance of addressing correlated noise, we contribute CIN-D, a novel correlated noise dataset captured across diverse illumination conditions to evaluate restoration methods on real rolling-shutter sensor noise. This dataset fills a critical gap in the literature for experimental evaluation with real-world correlated noise. Experiments on standard benchmarks with synthetic correlated noise and on CIN-D demonstrate that CARD consistently outperforms existing methods across denoising, deblurring, and super-resolution tasks.
Abstract (translated)
去噪扩散模型通过将图像恢复过程建模为一系列逐步去噪步骤，已经在图像恢复方面取得了最先进的性能。然而，大多数方法假设噪声是独立同分布（i.i.d.）高斯噪声，而实际情况中，由于读出机制的存在，真实世界的传感器常常会产生空间相关的噪声，这限制了现有模型的实际效果。 我们提出了CARD（Correlation Aware Restoration with Diffusion），这是一种无训练扩展的DDRM（Denoising Diffusion Restoration Model）方法，可以显式处理相关高斯噪声。CARD首先对带噪观察进行白化处理，将噪声转换为i.i.d.形式。然后，用去噪白化的更新替换原有的扩散恢复步骤，在继承DDRM闭合形式采样效率的同时，现在也可以处理相关的噪声。 为了强调解决相关噪声的重要性，我们贡献了一种新的数据集CIN-D（Correlated Illumination Noise Dataset），该数据集在不同的光照条件下捕捉到了真实滚动快门传感器噪声。这个数据集填补了现有文献中使用现实世界相关噪声进行实验评估的重要空白。 标准基准测试和合成的相关噪声以及CIN-D上的实验证明，CARD在去噪、去模糊和超分辨率任务上始终优于现有的方法。
URL
https://arxiv.org/abs/2512.05268
