Fundamentals of digital image processing
Fundamentals of digital image processing
PS estimation for image deblurring
CVGIP: Graphical Models and Image Processing
Robust identification of motion and out-of-focus blur parameters from blurred and noisy images
CVGIP: Graphical Models and Image Processing
Digital Image Restoration
Blur identification using the bispectrum
IEEE Transactions on Signal Processing
Efficient discrete spatial techniques for blur support identification in blind image deconvolution
IEEE Transactions on Signal Processing
On the cepstrum of two-dimensional functions (Corresp.)
IEEE Transactions on Information Theory
Simultaneous out-of-focus blur estimation and restoration for digital auto-focusing system
IEEE Transactions on Consumer Electronics
Image restoration in X-ray microscopy: PSF determination and biological applications
IEEE Transactions on Image Processing
Blur identification by the method of generalized cross-validation
IEEE Transactions on Image Processing
Blur identification by residual spectral matching
IEEE Transactions on Image Processing
On the accuracy of PSF representation in image restoration
IEEE Transactions on Image Processing
An efficient blind method for image quality measurement
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Perceptual visual quality metrics: A survey
Journal of Visual Communication and Image Representation
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In this paper, a criterion for objective defocus blur measurement is theoretically derived from one image. The essential idea is to estimate the point spread function (PSF) from the line spread function (LSF), whereas the LSF is constructed from edge information. It is proven that an edge point corresponds to the local maximal gradient in a blurred image, and therefore edges can be extracted from blurred images by conventional edge detectors. To achieve high accuracy, local Radon transform is implemented and a number of LSFs are extracted from each edge. The experimental results on a variety of synthetic and real blurred images validate the proposed method. The algorithm can be implemented for image quality evaluation in vision-based applications as no reference images are needed.