A Blur-SURE-Based Approach to Kernel Estimation for Motion Deblurring
- Autores: Li J.1
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Afiliações:
- School of Foreign Languages
- Edição: Volume 29, Nº 2 (2019)
- Páginas: 240-251
- Seção: Representation, Processing, Analysis, and Understanding of Images
- URL: https://journals.rcsi.science/1054-6618/article/view/195579
- DOI: https://doi.org/10.1134/S1054661819010164
- ID: 195579
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Resumo
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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Sobre autores
Jing Li
School of Foreign Languages
Autor responsável pela correspondência
Email: lijing2016@ruc.edu.cn
República Popular da China, Beijing, 100872
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