Two motion-blurred images are better than one
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Resolution enhancement via probabilistic deconvolution of multiple degraded images
Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
Blind blur assessment for vision-based applications
Journal of Visual Communication and Image Representation
A cross-validation framework for solving image restoration problems
Journal of Visual Communication and Image Representation
Information Sciences: an International Journal
Image deblurring with matrix regression and gradient evolution
Pattern Recognition
Model-based adaptive resolution upconversion of degraded images
Journal of Visual Communication and Image Representation
Joint MAP estimation for blind deconvolution: when does it work?
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
ABC optimized neural network model for image deblurring with its FPGA implementation
Microprocessors & Microsystems
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The point spread function (PSF) of a blurred image is often unknown a priori; the blur must first be identified from the degraded image data before restoring the image. Generalized cross-validation (GCV) is introduced to address the blur identification problem. The GCV criterion identifies model parameters for the blur, the image, and the regularization parameter, providing all the information necessary to restore the image. Experiments are presented which show that GVC is capable of yielding good identification results. A comparison of the GCV criterion with maximum-likelihood (ML) estimation shows the GCV often outperforms ML in identifying the blur and image model parameters