Zero-Shot Cell Image Super-Resolution

The shape of a cell is an important factor in cell examinations that diagnose cancer or certain disease, however, due to the limitations and nature of the microscope, low-resolution (LR) cell images can be obtained. LR images have limitations in analyzing the phenotype or morphological characteristics of cells. Therefore, they need to be restored to high-resolution (HR) images. In this paper, we propose a zero-shot super-resolution (ZSSR) algorithm to reconstruct cell shape information. In specific, a high-frequency filtering module (HFM) is adopted to calculate the difference between HR and LR by extracting various information such as the edge and corners of cells which are high-frequency information in an image. In addition, channel attention blocks (CAB) that suppress and emphasize feature information are used for SR without being confused with similar cell shapes in an image. It also improves the generalization performance of the network by sharing the network’s parameters. As a result, PSNR is improved by 0.04dB compared to that of the previous ZSSR. The source code will be made available at : https://github.com/JJeong-Gari/Cell-ZSSR/