Image restoration and reconstruction
Image restoration and reconstruction
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Smoothing: A General Tool for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in computer vision
Markov random field modeling in computer vision
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Information Theory and Data Compression
Introduction to Information Theory and Data Compression
Digital Picture Processing
Convergence Rates of Approximation by Translates
Convergence Rates of Approximation by Translates
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Edge-Preserving Image Denoising and Estimation of Discontinuous Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel blind deconvolution scheme for image restoration usingrecursive filtering
IEEE Transactions on Signal Processing
An overview of character recognition focused on off-line handwriting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An EM algorithm for wavelet-based image restoration
IEEE Transactions on Image Processing
Intensity based nonparametric image registration
Proceedings of the international conference on Multimedia information retrieval
Computational Statistics & Data Analysis
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Observed images are usually blurred versions of true images, due to imperfections of imaging devices, atmospheric turbulence, out of focus lens, motion blurs, and so forth. The major purpose of image deblurring is to restore the original image from its blurred version. A blurred image can be described by convolution of the original image with a point spread function (psf) that characterizes the blurring mechanism. Thus, one essential problem for image deblurring is to estimate the psf from the observed but blurred image, which turns out to be a challenging task, due to the ''ill-posed'' nature of the problem. In the literature, most existing image deblurring procedures assume that either the psf is completely known or it has a parametric form. Motivated by some image applications, including handwritten text recognition and calibration of imaging devices, we suggest a method for estimating the psf nonparametrically, in cases when the true image has one or more line edges, which is usually satisfied in the applications mentioned above and which is not a big restriction in some other image applications, because it is often convenient to take pictures of objects with line edges, using the imaging device under study. Both theoretical justifications and numerical studies show that the proposed method works well in applications.