caa a22 4500 467914672 CHVBK 20180406152858.0 cr unu---uuuuu 170328e20061201xx s 000 0 eng 10.1007/s11263-006-6468-1 doi (NATIONALLICENCE)springer-10.1007/s11263-006-6468-1 Image Deblurring in the Presence of Impulsive Noise [Elektronische Daten] [Leah Bar, Nahum Kiryati, Nir Sochen] Consider the problem of image deblurring in the presence of impulsive noise. Standard image deconvolution methods rely on the Gaussian noise model and do not perform well with impulsive noise. The main challenge is to deblur the image, recover its discontinuities and at the same time remove the impulse noise. Median-based approaches are inadequate, because at high noise levels they induce nonlinear distortion that hampers the deblurring process. Distinguishing outliers from edge elements is difficult in current gradient-based edge-preserving restoration methods. The suggested approach integrates and extends the robust statistics, line process (half quadratic) and anisotropic diffusion points of view. We present a unified variational approach to image deblurring and impulse noise removal. The objective functional consists of a fidelity term and a regularizer. Data fidelity is quantified using the robust modified L 1 norm, and elements from the Mumford-Shah functional are used for regularization. We show that the Mumford-Shah regularizer can be viewed as an extended line process. It reflects spatial organization properties of the image edges, that do not appear in the common line process or anisotropic diffusion. This allows to distinguish outliers from edges and leads to superior experimental results. Springer Science + Business Media, LLC, 2006 image deblurring nationallicence restoration nationallicence impulse noise nationallicence salt and pepper noise nationallicence variational methods nationallicence Bar Leah School of Electrical Engineering, Tel Aviv University, 69978, Tel Aviv, Israel aut Kiryati Nahum School of Electrical Engineering, Tel Aviv University, 69978, Tel Aviv, Israel aut Sochen Nir Dept. of Applied Mathematics, Tel Aviv University, 69978, Tel Aviv, Israel aut International Journal of Computer Vision Kluwer Academic Publishers 70/3(2006-12-01), 279-298 0920-5691 70:3<279 2006 70 11263 https://doi.org/10.1007/s11263-006-6468-1 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11263-006-6468-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Bar Leah School of Electrical Engineering, Tel Aviv University, 69978, Tel Aviv, Israel aut NATIONALLICENCE 700 1- Kiryati Nahum School of Electrical Engineering, Tel Aviv University, 69978, Tel Aviv, Israel aut NATIONALLICENCE 700 1- Sochen Nir Dept. of Applied Mathematics, Tel Aviv University, 69978, Tel Aviv, Israel aut NATIONALLICENCE 773 0- International Journal of Computer Vision Kluwer Academic Publishers 70/3(2006-12-01), 279-298 0920-5691 70:3<279 2006 70 11263 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer