Work

HD2Net: a deep learning framework for simultaneous denoising and deaberration in fluorescence microscopy

Year: 2025

Type: article

Abstract: Fluorescence microscopy is essential for biological research, offering high-contrast imaging of microscopic structures. However, the quality of these images is often compromised by optical aberrations and noise, particularly in low signal-to-noise ratio (SNR) conditions. While adaptive optics (AO) can correct aberrations, it requires costly hardware and slows down imaging, whereas current denoising approaches boost the SNR but leave out the aberration compensation. To address these limitations, we introduce HD2Net, a deep-learning framework that enhances image quality by simultaneously denoising and suppressing the effect of aberrations without the need for additional hardware. Building on our previous work, HD2Net incorporates noise estimation and aberration removal modules, effectively restoring images degraded by noise and aberrations. Through comprehensive evaluation of synthetic phantoms and biological data, we demonstrate that HD2Net outperforms existing methods, significantly improving image resolution and contrast. This framework offers a promising solution for enhancing biological imaging, particularly in challenging aberrating and low-light conditions.

Source: Optics Express

DOI: 10.1364/oe.554927

Authors: Xuekai Hou, Yue Li, Chad M. Hobson, Hari Shroff, Min Guo, Huafeng Liu

Institutions: Zhejiang University, Howard Hughes Medical Institute, Janelia Research Campus

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funders: Howard Hughes Medical Institute, National Natural Science Foundation of China, China Postdoctoral Science Foundation