arXiv Machine Learning By Xiangyu Qian, Jing Liu, Yunqing Tang, Luru Dai, Qiushi Li

A Zero-Shot Deep Image Prior Framework for Denoising and Deconvolution in Fluorescence Microscopy

Read the original on arXiv Machine Learning →

arXiv:2606. 28431v1 Announce Type: cross Abstract: Fluorescence microscopy images are degraded by noise and diffraction-induced blur, which compromise structural fidelity and limit quantitative analysis.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI
Jul 28

scMIR: a vision-language foundation model for single-cell light microscopy image representation

arXiv:2607. 22712v1 Announce Type: cross Abstract: Single-cell light microscopy images have become an important data source for characterizing cell phenotypes, but their complexity and heterogeneity pose challenges to high-throughput automated analysis.

By Yifan Shang (Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China, College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Jiahui Tan (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Xiangxiang Zeng (College of Computer Science and Electronic Engineering, Hunan University, Changsha, China), Renjie Zhou (Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China)