Linear motion blur parameter estimation in noisy images using fuzzy sets and power spectrum

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 directions (between 0 degrees and 180 degrees) and motion lengths (between 10 and 50 pixels). The results showed that the method works highly satisfactory for SNR > 22 dB and supports lower SNR compared with other algorithms.