Haoying Fu

Efficient Minimization Methods of Mixed l 2- l 1 and l 1- l 1 Norms for Image Restoration

Image restoration problems are often solved by finding the minimizer of a suitable objective function. Usually this function consists of a data-fitting term and a regularization term. For the least squares solution, both the data-fitting and the regularization terms are in the

Semi-supervised nonlinear dimensionality reduction

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A regularized structured total least squares algorithm for high-resolution image reconstruction

2 norm. In this paper, we consider the least absolute deviation (LAD) solution and the least mixed norm (LMN) solution. For the LAD solution, both the data-fitting and the regularization terms are in the

Incorporating query difference for learning retrieval functions in world wide web search

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Structured Total Least Squares for Color Image Restoration

1 norm. For the LMN solution, the regularization term is in the

Incorporating query difference for learning retrieval functions in information retrieval

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Fast algorithms for l1 norm/mixed l1 and l2 norms for image restoration

1 norm but the data-fitting term is in the

Efficient block noise removal based on nonlinear manifolds

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Fast High-Resolution Image Reconstruction Using Tikhonov Regularization Based Total Least Squares

2 norm. Since images often have nonnegative intensity values, the proposed algorithms provide the option of taking into account the nonnegativity constraint. The LMN and LAD solutions are formulated as the solution to a linear or quadratic programming problem which is solved by interior point methods. At each iteration of the interior point method, a structured linear system must be solved. The preconditioned conjugate gradient method with factorized sparse inverse preconditioners is employed to solve such structured inner systems. Experimental results are used to demonstrate the effectiveness of our approach. We also show the quality of the restored images, using the minimization of mixed

Fast algorithms for 11 norm/mixed 11 and 12 norms for image restoration

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