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Motion blur is one of the most common blurs that degrades images. Restoration of such images are highly dependent to estimation of motion blur parameters. Many researchers since 1976 have developed algorithms to estimate linear motion blur parameters. These algorithms are different in their performance, time complexity, precision and their robustness in noisy environments. In this paper we have presented a novel algorithm to estimate linear motion blur parameters such as direction and extend by using Radon transform to find direction and power spectrum modeling to find its extend. The most benefit of this algorithm is its robustness and precision in noisy images. This algorithm is a... 

In this paper, we present a new method for height finding in 2-D (Range-Azimuth) search Radars. The idea is based on the fact that the rate at which Doppler frequency of a target changes as target moves away from or toward the radar, depends on the height of the target for fixed target range and velocity. Radon transform is used to find the rate of the Doppler frequency changes using Doppler history of the target over several scans. Analysis for calculation of target height from its range, Doppler frequency, the rate of Doppler changes and radar operating frequency is given in detail. A computer simulation is also accomplished to illustrate the performance of the proposed method. ©2009 IEEE  

Motion blur is one of the most common blurs that degrades images. Restoration of such images is highly dependent on estimation of motion blur parameters. Since 1976, many researchers have developed algorithms to estimate linear motion blur parameters. These algorithms are different in their performance, time complexity, precision and robustness in noisy environments. In this paper, we have presented a novel algorithm to estimate linear motion blur parameters such as direction and length. We used Radon transform to find direction and bispectrum modeling to find the length of motion. Our algorithm is based on the combination of spatial and frequency domain analysis. The great benefit of our... 

Motion blur is one of the most common causes of image degradation. Restoration of such images is highly dependent on accurate estimation of motion blur parameters. To estimate these parameters, many algorithms have been proposed. These algorithms are different in their performance, time complexity, precision,and robustness in noisy environments. In this paper, we present a novel algorithm to estimate direction and length of motion blur,using Radon transform and fuzzy set concepts. The most important advantage of this algorithm is its robustness and precision in noisy images. This method was tested on a wide range of different types of standard images that were degraded with different... 

Motion blur is one of the most common blurs that degrades images. Restoration of such images are highly dependent to estimation of motion blur parameters. Many researchers have developed algorithms to estimate linear motion blur parameters. These algorithms are different in their performance, time complexity, precision and their robustness in noisy environments. In this paper we have presented a novel algorithm to estimate linear motion blur parameters such as direction and extend by using Radon transform to find direction and fuzzy set concepts to find its extend. The most benefit of this algorithm is its robustness and precision in noisy images. Our method was tested on a wide range of... 

This paper proposes a global motion estimation method to remove unintentional camera motions which degrade the visual quality of image sequences. The proposed approach is based on combination of 2D Radon transform, 1D Fourier transform and 1D Scale transform which can accurately estimate scale, rotational and translational distortions of camera motion and is robust to internal moving objects. Our experimental results with real and synthesized videos indicate the effectiveness of our proposed method  

Digital video stabilization is a category of techniques used to reduce the impact of unintentional camera motion such as jitter, jiggle, and other unsteady motions. These unintentional shakings degrade visual quality of videos and reduce the performance of subsequent processes such as video compression. Digital video stabilization which is performed by post processing the acquired frames, suffers from inaccuracy of motion estimation which is mostly due to the local motions of internal moving objects included in videos, and long processing time which prohibits them from being used in real time applications. In this paper we propose a fast and accurate transform based motion estimation method...