caa a22 4500 605541272 CHVBK 20210128100915.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00371-014-0920-y doi (NATIONALLICENCE)springer-10.1007/s00371-014-0920-y Non-blind deblurring of structured images with geometric deformation [Elektronische Daten] [Xin Zhang, Fuchun Sun, Guangcan Liu, Yi Ma] Non-blind deconvolution, which is to restore a sharp version of a given blurred image when the blur kernel is known, is a fundamental step in image deblurring. While the problem has been extensively studied, existing methods have conveniently ignored an important fact that deformation can significantly affect the statistical characteristics of an image and introduce additional blurring effect. In this paper, we show how to enhance non-blind deconvolution by recovering and undoing the deformation while deconvolving a given blurred image. We show that this is the case for almost all popular regularizers that have been proposed for image deblurring such as total variation and its variants. We conduct extensive simulations and experiments on real images and verify that the incorporation of geometric deformation in deconvolution can significantly improve the final deblurring results. Combined with existing blur kernel estimation techniques, our method can also be used to enhance blind image deblurring. Springer-Verlag Berlin Heidelberg, 2014 Non-Blind deconvolution nationallicence Geometric deformation nationallicence Total variation nationallicence Zhang Xin Department of Computer Science and Technology, Tsinghua University, Beijing, China aut Sun Fuchun Department of Computer Science and Technology, Tsinghua University, Beijing, China aut Liu Guangcan University of Illinois at Urbana-Champaign, Champaign, USA aut Ma Yi Microsoft Research Asia, Beijing, China aut The Visual Computer Springer Berlin Heidelberg 31/2(2015-02-01), 131-140 0178-2789 31:2<131 2015 31 371 https://doi.org/10.1007/s00371-014-0920-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-014-0920-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhang Xin Department of Computer Science and Technology, Tsinghua University, Beijing, China aut NATIONALLICENCE 700 1- Sun Fuchun Department of Computer Science and Technology, Tsinghua University, Beijing, China aut NATIONALLICENCE 700 1- Liu Guangcan University of Illinois at Urbana-Champaign, Champaign, USA aut NATIONALLICENCE 700 1- Ma Yi Microsoft Research Asia, Beijing, China aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/2(2015-02-01), 131-140 0178-2789 31:2<131 2015 31 371