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Phase contrast microscopy is a widely-used non-invasive technique for monitoring live cells over time. High-throughput biological experiments expect a wide-view (i.e., a low microscope magnification) to monitor the entire cell population and a high magnification on individual cell’s details, which is hard to achieve simultaneously. In this paper, we propose a cascaded refinement Generative Adversarial Network (GAN) for phase contrast microscopy image super-resolution. Our algorithm uses an optic-related data enhancement and super-resolves a phase contrast microscopy image in a coarse-to-fine fashion, with a new loss function consisting of a content loss and an adversarial loss. The proposed algorithm is both qualitatively and quantitatively evaluated on a dataset of 500 phase contrast microscopy images, showing its superior performance for super-resolving phase contrast microscopy images. The …