Open AccessCompressive fluorescence microscopy using saliency-guided sparse reconstruction ensemble fusion
Author(s) -
Shimon Schwartz,
Alexander Wong,
David A. Clausi
Publication year - 2012
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.20.017281
Subject(s) - compressed sensing , microscopy , fluorescence microscope , fusion , optics , noise (video) , materials science , fluorescence , signal to noise ratio (imaging) , artificial intelligence , computer science , pattern recognition (psychology) , physics , image (mathematics) , linguistics , philosophy
Compressive fluorescence microscopy has been proposed as a promising approach for fast acquisitions at sub-Nyquist sampling rates. Given that signal-to-noise ratio (SNR) is very important in the design of fluorescence microscopy systems, a new saliency-guided sparse reconstruction ensemble fusion system has been proposed for improving SNR in compressive fluorescence microscopy. This system produces an ensemble of sparse reconstructions using adaptively optimized probability density functions derived based on underlying saliency rather than the common uniform random sampling approach. The ensemble of sparse reconstructions are then fused together via ensemble expectation merging. Experimental results using real fluorescence microscopy data sets show that significantly improved SNR can be achieved when compared to existing compressive fluorescence microscopy approaches, with SNR increases of 16-9 dB within the noise range of 1.5%-10% standard deviation at the same compression rate.