Masked Pre-training Enables Universal Zero-shot Denoiser

TL;DR

This paper introduces MPI, a zero-shot denoising method that leverages masked pre-training to achieve high-quality image denoising without prior noise-specific training, demonstrating significant improvements over previous methods.

Contribution

The paper proposes a novel zero-shot denoising paradigm combining masked pre-training with iterative filling, enabling effective denoising on diverse noisy images without additional training.

Findings

MPI achieves superior denoising quality compared to previous methods.

MPI significantly reduces inference time for image denoising.

The approach generalizes well across various noise levels and image types.

Abstract

In this work, we observe that model trained on vast general images via masking strategy, has been naturally embedded with their distribution knowledge, thus spontaneously attains the underlying potential for strong image denoising. Based on this observation, we propose a novel zero-shot denoising paradigm, i.e., Masked Pre-train then Iterative fill (MPI). MPI first trains model via masking and then employs pre-trained weight for high-quality zero-shot image denoising on a single noisy image. Concretely, MPI comprises two key procedures: 1) Masked Pre-training involves training model to reconstruct massive natural images with random masking for generalizable representations, gathering the potential for valid zero-shot denoising on images with varying noise degradation and even in distinct image types. 2) Iterative filling exploits pre-trained knowledge for effective zero-shot denoising. It iteratively optimizes the image by leveraging pre-trained weights, focusing on alternate reconstruction of different image parts, and gradually assembles fully denoised image within limited number of iterations. Comprehensive experiments across various noisy scenarios underscore the notable advances of MPI over previous approaches with a marked reduction in inference time. Code available at https://github.com/krennic999/MPI.