Quantitative Zernike Phase-Contrast Microscopy with an Untrained Neural Network
DOI:
10.1364/jsapo.2024.16p_a37_3
Publication Date:
2024-12-24T17:12:49Z
AUTHORS (6)
ABSTRACT
In optical microscopy, Zernike phase-contrast microscopy (PCM) is a technique that transforms phase shifts in sample to contrast intensity by interference. Despite its wide usage many biological and clinical applications, it fails provide quantitative information about the specimen. One prior collaborative work [1] from our group managed add quantitativeness PCM retrieval algorithm based on compressive propagation. However, this relies heavily regularization non-trivial optimization tricks, severely limiting generalizability practical situations.
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