Zero-Shot Image Denoising for High-Resolution Electron Microscopy

TL;DR

This paper introduces Noise2SR, a zero-shot self-supervised denoising framework for high-resolution electron microscopy images, which effectively improves image quality using only a single noisy image without prior training data.

Contribution

The paper proposes a novel SR-based self-supervised training strategy with a Random Sub-sampler module for zero-shot denoising in electron microscopy, outperforming existing methods.

Findings

Outperforms state-of-the-art ZS-SSL methods in denoising quality.

Achieves comparable results to supervised denoising methods.

Effective in both simulated and real HREM denoising tasks.

Abstract

High-resolution electron microscopy (HREM) imaging technique is a powerful tool for directly visualizing a broad range of materials in real-space. However, it faces challenges in denoising due to ultra-low signal-to-noise ratio (SNR) and scarce data availability. In this work, we propose Noise2SR, a zero-shot self-supervised learning (ZS-SSL) denoising framework for HREM. Within our framework, we propose a super-resolution (SR) based self-supervised training strategy, incorporating the Random Sub-sampler module. The Random Sub-sampler is designed to generate approximate infinite noisy pairs from a single noisy image, serving as an effective data augmentation in zero-shot denoising. Noise2SR trains the network with paired noisy images of different resolutions, which is conducted via SR strategy. The SR-based training facilitates the network adopting more pixels for supervision, and the random sub-sampling helps compel the network to learn continuous signals enhancing the robustness. Meanwhile, we mitigate the uncertainty caused by random-sampling by adopting minimum mean squared error (MMSE) estimation for the denoised results. With the distinctive integration of training strategy and proposed designs, Noise2SR can achieve superior denoising performance using a single noisy HREM image. We evaluate the performance of Noise2SR in both simulated and real HREM denoising tasks. It outperforms state-of-the-art ZS-SSL methods and achieves comparable denoising performance with supervised methods. The success of Noise2SR suggests its potential for improving the SNR of images in material imaging domains.