Abstract

Fluorescent miscroscopy is a state-of-the-art method for creating high contrast and high resolution images of microscopic structures and has found wide application in microendoscopy (i.e., imaging cellular information from an optical probe within an animal). Cannula based microscopy methods have recently shown great promise for efficient microendoscopy imaging. Yet, performing real-time imaging with cannula methods have yet to be achieved due to the high computational complexity of the algorithms used for image reconstruction. We present an approach based on compressive sensing to improve computational speed and image reconstruction quality. We compare our approach with the state-of-the-art implementation based on direct binary search, a non-linear optimization technique. Results demonstrating up to 70 times improvement in the computation time and visual quality of the image over the direct binary search method are included in the paper.

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