Blind Image Deblurring based on Kernel Mixture

• URL: http://arxiv.org/abs/2101.06241v1
• Date: Fri, 15 Jan 2021 17:56:37 GMT
• Title: Blind Image Deblurring based on Kernel Mixture
• Authors: Sajjad Amrollahi Biyouki, Hoon Hwangbo
• Abstract summary: This paper regulates the structure of the blur kernel. We propose a kernel mixture structure while using the Gaussian kernel as a base kernel. A data-driven decision for the number of base kernels to combine makes the structure even more flexible.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Blind Image deblurring tries to estimate blurriness and a latent image out of a blurred image. This estimation, as being an ill-posed problem, requires imposing restrictions on the latent image or a blur kernel that represents blurriness. Different from recent studies that impose some priors on the latent image, this paper regulates the structure of the blur kernel. We propose a kernel mixture structure while using the Gaussian kernel as a base kernel. By combining multiple Gaussian kernels structurally enhanced in terms of scales and centers, the kernel mixture becomes capable of modeling nearly non-parametric shape of blurriness. A data-driven decision for the number of base kernels to combine makes the structure even more flexible. We apply this approach to a remote sensing problem to recover images from blurry images of satellite. This case study shows the superiority of the proposed method regulating the blur kernel in comparison with state-of-the-art methods that regulates the latent image.

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