Paper
20 March 2015 Total variation based image deconvolution for extended depth-of-field microscopy images
F. Hausser, I. Beckers, M. Gierlak, O. Kahraman
Author Affiliations +
Abstract
One approach for a detailed understanding of dynamical cellular processes during drug delivery is the use of functionalized biocompatible nanoparticles and fluorescent markers. An appropriate imaging system has to detect these moving particles so as whole cell volumes in real time with high lateral resolution in a range of a few 100 nm. In a previous study Extended depth-of-field microscopy (EDF-microscopy) has been applied to fluorescent beads and tradiscantia stamen hair cells and the concept of real-time imaging has been proved in different microscopic modes. In principle a phase retardation system like a programmable space light modulator or a static waveplate is incorporated in the light path and modulates the wavefront of light. Hence the focal ellipsoid is smeared out and images seem to be blurred in a first step. An image restoration by deconvolution using the known point-spread-function (PSF) of the optical system is necessary to achieve sharp microscopic images of an extended depth-of-field. This work is focused on the investigation and optimization of deconvolution algorithms to solve this restoration problem satisfactorily. This inverse problem is challenging due to presence of Poisson distributed noise and Gaussian noise, and since the PSF used for deconvolution exactly fits in just one plane within the object. We use non-linear Total Variation based image restoration techniques, where different types of noise can be treated properly. Various algorithms are evaluated for artificially generated 3D images as well as for fluorescence measurements of BPAE cells.
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F. Hausser, I. Beckers, M. Gierlak, and O. Kahraman "Total variation based image deconvolution for extended depth-of-field microscopy images", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941329 (20 March 2015); https://doi.org/10.1117/12.2080813
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Cited by 3 scholarly publications.
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KEYWORDS
Point spread functions

Deconvolution

Image restoration

Signal to noise ratio

Microscopy

Imaging systems

3D image processing

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