Abstract :
Fluorescence microscopy imaging is widely used in biomedical research, astronomical speckle imaging, remote sensing, positron-emission tomography, and many other applications. In companion papers P. Sarder and A. Nchorai, we developed a maximum likelihood (ML)-based image deconvolution technique to quantify fluorescence signals from a three-dimensional (3D) image of a target captured microparticle ensemble. We assumed both the additive Gaussian and Poisson statistics for the noise. Imaging is performed by using a confocal fluorescence microscope system. Potential application of microarray technology includes security, environmental monitoring, analyzing assays for DNA or protein targets, functional genomics, and drug development. We proposed a new parametric model of the fluorescence microscope 3D point-spread function (PSF) in terms of basis functions. In this paper, we present a performance analysis of the ML-based deconvolution techniques (P. Sarder and A. Nchorai) for both the noise models
Keywords :
Gaussian processes; fluorescence; image processing; maximum likelihood estimation; optical images; optical microscopy; 3D point-spread function; Poisson statistics; additive Gaussian; confocal fluorescence microscope system; image deconvolution technique; maximum likelihood technique; microscopy images; quantifying fluorescence; target-captured microparticles; three-dimensional image; Additive noise; Biomedical imaging; Deconvolution; Fluorescence; Microscopy; Parametric statistics; Performance analysis; Remote sensing; Speckle; Tomography;
