Determining the regularization parameters for super-resolution problems
Signal Processing
A new method for parameter estimation of edge-preserving regularization in image restoration
Journal of Computational and Applied Mathematics
Automatic noise estimation in images using local statistics. Additive and multiplicative cases
Image and Vision Computing
Semi-blind image restoration using a local neural approach
Neurocomputing
Optimal restoration of multichannel images based on constrained mean-square estimation
Journal of Visual Communication and Image Representation
Iterative evaluation of the regularization parameter in regularized image restoration
Journal of Visual Communication and Image Representation
A cross-validation framework for solving image restoration problems
Journal of Visual Communication and Image Representation
Performance of reconstruction-based super-resolution with regularization
Journal of Visual Communication and Image Representation
Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks
Pattern Recognition Letters
Error concealment by means of motion refinement and regularized bregman divergence
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
A learning-based method for compressive image recovery
Journal of Visual Communication and Image Representation
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The application of regularization to ill-conditioned problems necessitates the choice of a regularization parameter which trades fidelity to the data with smoothness of the solution. The value of the regularization parameter depends on the variance of the noise in the data. The problem of choosing the regularization parameter and estimating the noise variance in image restoration is examined. An error analysis based on an objective mean-square-error (MSE) criterion is used to motivate regularization. Two approaches for choosing the regularization parameter and estimating the noise variance are proposed. The proposed and existing methods are compared and their relationship to linear minimum-mean-square-error filtering is examined. Experiments are presented that verify the theoretical results