A new and general method for blind shift-variant deconvolution of biomedical images

Authors:
Moritz Blume;Darko Zikic;Wolfgang Wein;Nassir Navab
Affiliations:
Computer Aided Medical Procedures, Technische Universität München, Germany;Computer Aided Medical Procedures, Technische Universität München, Germany;Computer Aided Medical Procedures, Technische Universität München, Germany and Siemens Corporate Research, Princeton;Computer Aided Medical Procedures, Technische Universität München, Germany
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2007

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Abstract

We present a new method for blind deconvolution of multiple noisy images blurred by a shift-variant point-spread-function (PSF). We focus on a setting in which several images of the same object are available, and a transformation between these images is known. This setting occurs frequently in biomedical imaging, for example in microscopy or in medical ultrasound imaging. By using the information from multiple observations, we are able to improve the quality of images blurred by a shift-variant filter, without prior knowledge of this filter. Also, in contrast to other work on blind and shift-variant deconvolution, in our approach no parametrization of the PSF is required. We evaluate the proposed method quantitatively on synthetically degraded data as well as qualitatively on 3D ultrasound images of liver. The algorithm yields good restoration results and proves to be robust even in presence of high noise levels in the images.