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Báo cáo hóa học: " Research Article Linear Motion Blur Parameter Estimation in Noisy Images Using Fuzzy Sets and Power Spectrum"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Linear Motion Blur Parameter Estimation in Noisy Images Using Fuzzy Sets and Power Spectrum | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 68985 8 pages doi 10.1155 2007 68985 Research Article Linear Motion Blur Parameter Estimation in Noisy Images Using Fuzzy Sets and Power Spectrum Mohsen Ebrahimi Moghaddam and Mansour Jamzad Department of Computer Engineering Sharif University of Technology 11365-8639 Tehran Iran Received 17 July 2005 Revised 11 March 2006 Accepted 15 March 2006 Recommended by Rafael Molina 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 and 180 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. Copyright 2007 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION The aim of image restoration is to reconstruct or estimate an uncorrupted image by using the degraded version of the same image. One of the most common degradation functions is linear motion blur with additive noise. Equation 1 shows the relationship between the observed image g x y and its uncorrupted version f x y 1 g x y f x y h x y n x y . 1 In this equation h is the blurring function or point spread function PSF that is convolved in the original image and n is the additive noise function. According to 1 in order to determine the .