Blur identification using averaged spectra of degraded image singular vectors
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
Blur identification using the bispectrum
IEEE Transactions on Signal Processing
A recursive soft-decision approach to blind image deconvolution
IEEE Transactions on Signal Processing
Motion blur adaptive identification from natural image model
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A qualitative-quantitative comparison of image motion deblurring algorithms
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Directional high-pass filter for blurry image analysis
Image Communication
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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 algorithm is its robustness and precision in noisy images. We used statistical measures to prove goodness of our model. Our method was tested on 80 standard images that were degraded with different directions and motion lengths, with additive Gaussian noise. The error tolerance average of the estimated parameters was 0.9^o in direction and 0.95 pixel in length and the standard deviations were 0.69 and 0.85, respectively.