Abstract
Blind motion deblurring from a single image is essentially an ill-posed problem that requires regularization to solve. In this paper, we introduce a new type of an efficient and fast method for the estimation of the motion blur-kernel, through a bi-lp-norm regularization applied on both the sharp image and the blur kernel in the MAP framework. Without requiring any prior information of the latent image and the blur kernel, our proposed approach is able to restore high-quality images from given blurred images. Moreover a fast numerical scheme is used for alternatingly caculating the sharp image and the blur-kernel, by combining the split Bregman method and look-up table trick. Experiments on both sythesized and real images revealed that our algorithm can compete with much more sophisticated state-of-the-art methods.
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Gan, W., Zhou, Y., He, L. (2016). Bi-Lp-Norm Sparsity Pursuiting Regularization for Blind Motion Deblurring. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_81
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DOI: https://doi.org/10.1007/978-3-319-46672-9_81
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