Recently, whole slide imaging (WSI) has become the gold standard for the clinical diagnosis of various diseases. It not only strengthens the cooperation between the computer and pathologists, but also promotes the development of remote primary diagnosis. WSI usually uses 20x and 40x objective lens scanners to scan the slides. Compared with the low-magnification lens, the high-magnification lens could provide high-resolution (HR) images, while sacrificing the large field-of-view (FOV), large depth-of-field (DOF), and high scanning efficiency instead. In this paper, we propose an image super-resolution (SR) reconstruction method based on a deep residual network (DSN), which improves flexibility and recovers the effective information neglected by the conventional network. After experimental verification, our method achieves large FOV, high scanning efficiency, and HR images simultaneously for assisting the diagnosis of pathologists.
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