most citedMasked and Shuffled Blind Spot Denoising for Real-World Images

1 citations · 1 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2025

FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matching

Alp Eren Sari, Paolo Favaro

We propose FlowCut, a simple and capable method for unsupervised video instance segmentation consisting of a three-stage framework to construct a high-quality video dataset with ps…

cs.CV2025

Invert2Restore: Zero-Shot Degradation-Blind Image Restoration

Hamadi Chihaoui, Paolo Favaro

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 operat…

cs.CV2025

Diffusion Image Prior

Hamadi Chihaoui, Paolo Favaro

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…

cs.CV2025

Unsupervised Real-World Denoising: Sparsity is All You Need

Hamadi Chihaoui, Paolo Favaro

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…

cs.CV2024

Blind Image Restoration via Fast Diffusion Inversion

Hamadi Chihaoui, Abdelhak Lemkhenter, Paolo Favaro

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…

cs.CV20241 cited

Masked and Shuffled Blind Spot Denoising for Real-World Images

Hamadi Chihaoui, Paolo Favaro

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…