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. For natural image deblurring, deep convolutional neural networks (CNNs) achieve promising performance. But they usually suffer from large model sizes, heavy computation costs, or small throughput, which are critical for high-throughput microscopy image deblurring. To address those problems, we collect HQ electron microscopy and histology datasets and propose a graph reasoning attention network (GRAN). Specifically, we treat deep feature points as embedded visual components, build a graph describing the relationship between all pairs of visual components, and perform reasoning in the graph with a graph convolutional network. The reasoning results are then transferred as attention and residual learning is introduced to achieve graph reasoning attention block (GRAB). We conduct extensive experiments to demonstrate the effectiveness of our GRAN.