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The main aim of this paper is to study total variation (TV) regularization in deblurring and sparse unmixing of hyperspectral images. In the model we also incorporate blurring operators for dealing with blurring effects, and in particular, blurring operators for hyperspectral imaging whose PSFs are generally system dependent and result from axial optical(More)
In this paper, we address the total variation (TV)-based nonlinear image restoration problems. In nonlinear image restoration problems, an original image is corrupted by a spatially-invariant blur, the build-in nonlinearity in imaging system, and the additive Gaussian white noise. We study the objective function consisting of the nonlinear least squares(More)
In this paper, we study the problem of recovering a tensor with missing data. We propose a new model combining the total variation regularization and low-rank matrix factorization. A block coordinate decent (BCD) algorithm is developed to efficiently solve the proposed optimization model. We theoretically show that under some mild conditions, the algorithm(More)
Exemplar-based algorithms are a popular technique for image inpainting. They mainly have two important phases: deciding the filling-in order and selecting good exemplars. Traditional exemplar-based algorithms are to search suitable patches from source regions to fill in the missing parts, but they have to face a problem: improper selection of exemplars. To(More)
In signal and image processing, we want to recover a faithful representation of an original scene from blurred, noisy image data. This process can be transformed mathematically into solving a linear system with a blurring matrix. Particularly, the blurring matrix is determined from not only a point spread function (PSF), which defines how each pixel is(More)