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 state-of-the-art results compared to existing self-supervised denoising methods. Website: https://hamadichihaoui.github.io/mash.

We present a self-supervised pre-training scheme for single image denoising based on a novel pretext task. Our work is inspired by the success of self-supervised learning (SSL) methods in transfer learning. These methods have been shown to be extremely effective when used to pre-train a model that is then fine-tuned on small datasets. As pretext task, we propose to train a denoising network on patches of the downsampled input image, which we treat as pseudo-clean image patches, and an adaptive noise estimator to learn the specific noise distribution of the input image. By carrying out the pre-training on the single input image, rather than on a separate dataset, we avoid the well-known noise distribution gap between images in the training dataset and the single input image used at test time. We evaluate our SSL method for single image denoising via extensive experiments on both synthetic and real-world noisy image datasets. We demonstrate SotA results compared to existing unsupervised denoising methods, by transferring our pre-training to IDR [1], thus showing that SSL pre-training is a promising framework also in image denoising. Website: https://hamadichihaoui.github.io/SSL-Denoising.

Image Restoration (IR) methods based on a pre-trained diffusion model have demonstrated state-of-the-art performance. However, they have two fundamental limitations: 1) they often assume that the degradation operator is completely known and 2) they alter the diffusion sampling process, which may result in restored images that do not lie onto 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.