Diffusion Image Prior

Zero-shot image restoration (IR) methods based on pretrained diffusion models have recently achieved significant success. These methods typically require at least a parametric form of the degradation model. However, in real-world scenarios, the degradation may be too complex to define explicitly. To handle this general case, we introduce the Diffusion Image Prior (DIIP). We take inspiration from the Deep Image Prior (DIP)[16], since it can be used to remove artifacts without the need for an explicit degradation model. However, in contrast to DIP, we find that pretrained diffusion models offer a much stronger prior, despite being trained without knowledge from corrupted data. We show that, the optimization process in DIIP first reconstructs a clean version of the image before eventually overfitting to the degraded input, but it does so for a broader range of degradations than DIP. In light of this result, we propose a blind image restoration (IR) method based on early stopping, which does not require prior knowledge of the degradation model. We validate DIIP on various degradation-blind IR tasks, including JPEG artifact removal, waterdrop removal, denoising and super-resolution with state-of-the-art results.

Invert2Restore: Zero-Shot Degradation-Blind Image Restoration

Two of the main challenges of image restoration in real-world scenarios are the accurate characterization of an image prior and the precise modeling of the image degradation operator. Pre-trained diffusion models have been very successfully used as image priors in zero-shot image restoration methods. However, how to best handle the degradation operator is still an open problem. In real-world data, methods that rely on specific parametric assumptions about the degradation model often face limitations in their applicability. To address this, we introduce Invert2Restore, a zero-shot, training-free method that operates in both fully blind and partially blind settings -- requiring no prior knowledge of the degradation model or only partial knowledge of its parametric form without known parameters. Despite this, Invert2Restore achieves high-fidelity results and generalizes well across various types of image degradation. It leverages a pre-trained diffusion model as a deterministic mapping between normal samples and undistorted image samples. The key insight is that the input noise mapped by a diffusion model to a degraded image lies in a low-probability density region of the standard normal distribution. Thus, we can restore the degraded image by carefully guiding its input noise toward a higher-density region. We experimentally validate Invert2Restore across several image restoration tasks, demonstrating that it achieves state-of-the-art performance in scenarios where the degradation operator is either unknown or partially known.

Unsupervised Real-World Denoising: Sparsity is All You Need

Supervised training for real-world denoising presents challenges due to the difficulty of collecting large datasets of paired noisy and clean images. Recent methods have attempted to address this by utilizing unpaired datasets of clean and noisy images. Some approaches leverage such unpaired data to train denoisers in a supervised manner by generating synthetic clean-noisy pairs. However, these methods often fall short due to the distribution gap between synthetic and real noisy images. To mitigate this issue, we propose a solution based on input sparsification, specifically using random input masking. Our method, which we refer to as Mask, Inpaint and Denoise (MID), trains a denoiser to simultaneously denoise and inpaint synthetic clean-noisy pairs. On one hand, input sparsification reduces the gap between synthetic and real noisy images. On the other hand, an inpainter trained in a supervised manner can still accurately reconstruct sparse inputs by predicting missing clean pixels using the remaining unmasked pixels. Our approach begins with a synthetic Gaussian noise sampler and iteratively refines it using a noise dataset derived from the denoiser's predictions. The noise dataset is created by subtracting predicted pseudo-clean images from real noisy images at each iteration. The core intuition is that improving the denoiser results in a more accurate noise dataset and, consequently, a better noise sampler. We validate our method through extensive experiments on real-world noisy image datasets, demonstrating competitive performance compared to existing unsupervised denoising methods.

Blind Image Restoration via Fast Diffusion Inversion

Recently, various methods have been proposed to solve Image Restoration (IR) tasks using a pre-trained diffusion model leading to state-of-the-art performance. However, most of these methods assume that the degradation operator in the IR task is completely known. Furthermore, a common characteristic among these approaches is that they alter the diffusion sampling process in order to satisfy the consistency with the degraded input image. This choice has recently been shown to be sub-optimal and to cause the restored image to deviate from the data manifold. To address these issues, we propose Blind Image Restoration via fast Diffusion inversion (BIRD) a blind IR method that jointly optimizes for the degradation model parameters and the restored image. To ensure that the restored images lie onto the data manifold, we propose a novel sampling technique on a pre-trained diffusion model. A key idea in our method is not to modify the reverse sampling, i.e., not to alter all the intermediate latents, once an initial noise is sampled. This is ultimately equivalent to casting the IR task as an optimization problem in the space of the input noise. Moreover, to mitigate the computational cost associated with inverting a fully unrolled diffusion model, we leverage the inherent capability of these models to skip ahead in the forward diffusion process using large time steps. We experimentally validate BIRD on several image restoration tasks and show that it achieves state of the art performance on all of them. Our code is available at https://github.com/hamadichihaoui/BIRD.

Masked and Shuffled Blind Spot Denoising for Real-World Images

We introduce a novel approach to single image denoising based on the Blind Spot Denoising principle, which we call MAsked and SHuffled Blind Spot Denoising (MASH). We focus on the case of correlated noise, which often plagues real images. MASH is the result of a careful analysis to determine the relationships between the level of blindness (masking) of the input and the (unknown) noise correlation. Moreover, we introduce a shuffling technique to weaken the local correlation of noise, which in turn yields an additional denoising performance improvement. We evaluate MASH via extensive experiments on real-world noisy image datasets. We demonstrate on par or better results compared to existing self-supervised denoising methods.