2016Bajic Denoising: Difference between revisions
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== Citation == | == Citation == | ||
Bajic;, B.; Lindblad, J. & Sladoje, N. Blind restoration of images degraded with mixed Poisson-Gaussian noise with application in transmission electron microscopy. Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, 2016, 123-127 | |||
== Abstract == | == Abstract == | ||
Latest revision as of 08:29, 10 May 2018
Citation
Bajic;, B.; Lindblad, J. & Sladoje, N. Blind restoration of images degraded with mixed Poisson-Gaussian noise with application in transmission electron microscopy. Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, 2016, 123-127
Abstract
Noise and blur, present in images after acquisition, negatively affect their further analysis. For image enhancement when the Point Spread Function (PSF) is unknown, blind deblurring is suitable, where both the PSF and the original image are simultaneously reconstructed. In many realistic imaging conditions, noise is modelled as a mixture of Poisson (signal-dependent) and Gaussian (signal independent) noise. In this paper we propose a blind deconvolution method for images degraded by such mixed noise. The method is based on regularized energy minimization. We evaluate its performance on synthetic images, for different blur kernels and different levels of noise, and compare with non-blind restoration. We illustrate the performance of the method on Transmission Electron Microscopy images of cilia, used in clinical practice for diagnosis of a particular type of genetic disorders.
Keywords
Links
http://ieeexplore.ieee.org/document/7493226/