‘High-Throughput Microscopy Image Deblurring With Graph Reasoning Attention Network’

“High-quality (HQ) microscopy images afford more detailed information for modern life science research and quantitative image analyses. However, in practice, HQ microscopy images are not commonly available or suffer from blurring artifacts. Compared with natural images, such low-quality (LQ) microscopy ones often share some visual characteristics: more complex structures, less informative background, and repeating patterns. … To address those problems, we collect HQ electron microscopy and histology datasets and propose a graph reasoning attention network (GRAN).”

Find the paper and full list of authors in the 2023 IEEE 20th International Symposium on Biomedical Imaging proceedings.

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