June 5, 2024, 4:49 a.m. | Minghong Duan, Linhao Qu, Zhiwei Yang, Manning Wang, Chenxi Zhang, Zhijian Song

cs.CV updates on arXiv.org arxiv.org

arXiv:2401.15613v2 Announce Type: replace-cross
Abstract: High-quality whole-slide scanners are expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution pathology whole-slide images in daily clinical work. Deep learning-based single-image super-resolution techniques are an effective way to solve this problem by synthesizing high-resolution images from low-resolution ones. However, the existing super-resolution models applied in pathology images can only work in fixed integer magnifications, significantly decreasing their applicability. Though methods based on implicit neural representation have shown promising results in …

abstract acquisition arxiv clinical cs.cv daily deep learning eess.iv framework image images low pathology problem quality replace resolution scale scanners solve texture type via work

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