Abstract
Blind motion deblurring is a highly challenging inverse problem in image processing and low-level computer vision. In this paper, we propose a novel approach to identify the parameters (blur length and orientation) of motion blur from an observed image. The kernel estimation is based on a novel criterion — the minimization of a blurred Stein’s unbiased risk estimate (blur-SURE): an unbiased estimate of a filtered mean squared error. By incorporating a simple Wiener filtering into the blur-SURE, the motion blur is estimated by minimizing this new objective functional with high accuracy. We then perform non-blind deconvolution using the high-quality SURE-LET algorithm with the estimated kernel. The results of synthetic and real experiments are quite competitive with other state-of-the-art algorithms under a wide range of degradation scenarios both numerically and visually.
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Jing Li was born in Hebei, China, in 1986. She received her Bachelor degree from School of Foreign Languages, Peking University, in 2011; Master degree in Translation and Interpretation in Public Services from Faculty of Humanities, Universidad de Alcalá, in 2012, and PhD in Humanities from Universidad Carlos III de Madrid, in 2015. She is currently with School of Foreign Languages, Renmin University of China, Beijing, China. Her main research interests include computational linguistics, corpus-based analysis, data processing and analysis.
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Li, J. A Blur-SURE-Based Approach to Kernel Estimation for Motion Deblurring. Pattern Recognit. Image Anal. 29, 240–251 (2019). https://doi.org/10.1134/S1054661819010164
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DOI: https://doi.org/10.1134/S1054661819010164