Refocusing Phase Contrast Microscopy Images
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.
L. Han and Z. Yin, "Refocusing Phase Contrast Microscopy Images," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10434 LNCS, pp. 65-74, Springer Verlag, Sep 2017.
The definitive version is available at https://doi.org/10.1007/978-3-319-66185-8_8
Intelligent Systems Center
Keywords and Phrases
Medical Computing; Visualization; Effective Algorithms; Image Content; Image Gradients; Microscopy Image Analysis; Noninvasive Technique; Phase-Contrast Microscopy; Phase-Contrast Microscopy Images; Sparse Prior; Medical Imaging
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