Refocusing phase contrast microscopy images

Abstract

Phase contrast microscopy is a very popular non-invasive technique for monitoring live cells. However, its images can be blurred if optics are imperfectly aligned and the visualization on specimen details can be affected by noisy background. We propose an effective algorithm to refocus phase contrast microscopy images from two perspectives: optics and specimens. First, given a defocused image caused by misaligned optics, we estimate the blur kernel based on the sparse prior of dark channel, and non-blindly refocus the image with the hyper-Laplacian prior of image gradients. Then, we further refocus the image contents on specimens by removing the artifacts from the background, which provides a sharp visualization on fine specimen details. The proposed algorithm is both qualitatively and quantitatively evaluated on a dataset of 500 phase contrast microscopy images, showing its superior performance for visualizing specimens and facilitating microscopy image analysis.