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Blind deblurring attempts to recover the latent sharp image from a blurred one. Such task is a well-known ill-posed inverse problem and is therefore usually solved as a posteriori probability estimation, incorporating prior information on natural images. In this paper, we propose a general blind noisy deblurring model based on hyper Laplacian (HL) in gradient domain and kernel spectra prior. This...
Removing the noise while keeping the image features like edges, textures is a challenging problem in image denoising. Because it is an under-determined problem, defining appropriate image priors to regularize the problem plays an important role. Recently a popular one among proposed image priors is the graph Laplacian regularizer, which can exploit the local geometry structure of the image. Introducing...
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