@article {69719, title = {Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopyAbstract}, journal = {Nature Communications}, volume = {16}, year = {2025}, month = {Jan-12-2025}, abstract = {

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained {\textquoteleft}de-aberration{\textquoteright} networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in\ C. elegans\ embryos.

}, doi = {10.1038/s41467-024-55267-x}, url = {https://www.nature.com/articles/s41467-024-55267-xhttps://www.nature.com/articles/s41467-024-55267-x.pdfhttps://www.nature.com/articles/s41467-024-55267-x.pdfhttps://www.nature.com/articles/s41467-024-55267-x}, author = {Guo, Min and Wu, Yicong and Hobson, Chad M. and Su, Yijun and Qian, Shuhao and Krueger, Eric and Christensen, Ryan and Kroeschell, Grant and Bui, Johnny and Chaw, Matthew and Zhang, Lixia and Liu, Jiamin and Hou, Xuekai and Han, Xiaofei and Lu, Zhiye and Ma, Xuefei and Zhovmer, Alexander and Combs, Christian and Moyle, Mark and Yemini, Eviatar and Liu, Huafeng and Liu, Zhiyi and Benedetto, Alexandre and La Riviere, Patrick and Col{\'o}n-Ramos, Daniel and Shroff, Hari} } @article {69720, title = {HD2Net: A Deep Learning Framework for Simultaneous Denoising and Deaberration in Fluorescence Microscopy}, journal = {bioRxiv}, year = {2025}, month = {01/2025}, 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.

}, doi = {10.1101/2025.01.06.631475}, url = {https://www.biorxiv.org/content/early/2025/01/08/2025.01.06.631475}, author = {Hou, Xuekai and Li, Yue and Hobson, Chad M. and Shroff, Hari and Guo, Min and Liu, Huafeng} } @article {69734, title = {HD2Net: A Deep Learning Framework for Simultaneous Denoising and Deaberration in Fluorescence Microscopy}, journal = {bioRxiv}, year = {2025}, month = {2025 Jan 8}, 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.

}, doi = {10.1101/2025.01.06.631475}, url = {https://www.biorxiv.org/content/early/2025/01/08/2025.01.06.631475}, author = {Hou, Xuekai and Li, Yue and Hobson, Chad M. and Shroff, Hari and Guo, Min and Liu, Huafeng} }