A defocus blur metric for use in blind image quality assessment is proposed. Blind image deconvolution methods are used to determine the metric. Existing direct deconvolution methods based on the cepstrum, bicepstrum and on a spectral subtraction technique are compared across 210 images. A variation of the spectral subtraction method, based on a power spectrum surface of revolution, is proposed...

Blind image deconvolution, a technique for obtaining restored as well the blur kernel from an inexact image. This research uses spatial characteristics to tackle problem of blind deconvolution. To work, proposed method does not necessitate prior information about kernel. Many applications, such remote sensing, astronomy, and medical X-ray imaging, deconvolution algorithms. study used maximum po...

In this Diploma Thesis we will present some methods to improve the quality of a given pictures. In particular blind deconvolution will be applied to deblurr the images. The deconvolution tries to invert the blurring of an image that is modeled by the convolution g = f ∗ h. Blind deconvolution tries to do this without knowledge of the point spread function that blurred the image. In the Thesis f...

Algorithms for image recovery with super-resolution from sequences of short-exposure images are presented in this paper. Both deconvolution from wavefront sensing (DWFS) and blind deconvolution are explored. A multiframe algorithm is presented for DWFS which is based on maximum a posteriori (MAP) formulation. A multiframe blind deconvolution algorithm is presented based on a maximum likelihood ...

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This paper deals with the simultaneous identi cation of the blur and the restoration of a noisy and blurred image. We propose the use of Dirichlet distributions to model our prior knowledge about the blurring function together with smoothness constraints on the restored image to solve the blind deconvolution problem. We show that the use of Dirichlet distributions o ers a lot of exibility in in...

A new blind deconvolution method is proposed for recovering an unknown source signal, which is observed through two unknown channels characterized by non-minimum phase impulse response filters. Conventional methods cannot estimate the nonminimum phase parts. Our method is based on computing the eigenvector corresponding to the smallest eigenvalue of the input correlation matrix and using a cost...

In this paper, the problem of blind deconvolution of dynamical systems is considered using a state space approach. A balanced parameterized canonical form is used as a model for the underlying dynamical system instead of the more common controller or observable canonical form. The results are compared with those obtained using a controller canonical form. It is shown experimentally that using t...

A measure of temporal predictability is de-ned, and used for blind deconvolution of sound signals. The method is based on the observation that physical environments act as smoothing -lters, and therefore increase the predictability of signals. These smoothing e0ects can be reversed by a deconvolution -lter which minimises a measure of temporal predictability. This -lter is obtained as the close...

This paper deals with a blind deconvolution (DB) problem for multiple-inputmultiple-output infinite impulse response (MIMO-IIR) systems. To solve this problem, we propose an eigenvector algorithm (EVA). In the proposed EVA, two kinds of EVAs are merged so as to give a good performance: One is an EVA and the other is a Robust EVA (REVA)which works with as little sensitive to Gaussian noise as po...