Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Digital Image Restoration
Digital Image Restoration
A novel blind deconvolution scheme for image restoration usingrecursive filtering
IEEE Transactions on Signal Processing
A regularization approach to joint blur identification and image restoration
IEEE Transactions on Image Processing
Total variation blind deconvolution
IEEE Transactions on Image Processing
General choice of the regularization functional in regularized image restoration
IEEE Transactions on Image Processing
Iterative PSF estimation and its application to shift invariant and variant blur reduction
EURASIP Journal on Advances in Signal Processing
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Spatially adaptive intensity bounds on the image estimate are shown to be an effective means of regularising the ill-posed image restoration problem. For blind restoration, the local intensity constraints also help to further define the solution, thereby reducing the number of multiple solutions and local minima. The bounds are defined in terms of the local statistics of the image estimate and a control parameter which determines the scale of the bounds. Guidelines for choosing this parameter are developed in the context of classical (nonblind) image restoration. The intensity bounds are applied by means of the gradient projection method, and conditions for convergence are derived when the bounds are refined using the current image estimate. Based on this method, a new alternating constrained minimisation approach is proposed for blind image restoration. On the basis of the experimental results provided, it is found that local intensity bounds offer a simple, flexible method of constraining both the nonblind and blind restoration problems.