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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)
The finite difference scheme with the shifted Grünwarld formula is employed to semi-discrete the fractional diffusion equations. This spatial discretization can reduce to the large system of ordinary differential equations (ODEs) with initial values. Recently, boundary value method (BVM) was developed as a popular algorithm for solving large systems of(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)