Zero-Shot Image Denoising for High-Resolution Electron Microscopy

arXiv:2406.14264v1 Announce Type: cross
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 …

abstract arxiv availability challenges cs.cv data denoising eess.iv electron however image imaging low materials microscopy noise resolution self-supervised learning signal space ssl supervised learning tool type work zero-shot