0548672 20230418204410.020211125235959.9 85110438127 000677493500009 10.1038/s41598-021-93636-4 13 s. cav_un_epca*03865942045-232211Nature Publishing Group motion blur estimating motion parameters medical ultrasound imagesBarm cav_un_auth*0347018 Honarvar Shakibaei Asli Barmak UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing MY Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0223368 Zhao Y. CN cav_un_auth*0377796 Erkoyuncu J. A. GB http://library.utia.cas.cz/separaty/2021/ZOI/honarvar-0548672.pdf https://www.nature.com/articles/s41598-021-93636-4 GJ18-26018Y GA ČR cav_un_auth*0360230 High-quality medical ultrasound imaging is definitely concerning motion blur, while medical image analysis requires motionless and accurate data acquired by sonographers. The main idea of this paper is to establish some motion blur invariant in both frequency and moment domain to estimate the motion parameters of ultrasound images. We propose a discrete model of point spread function of motion blur convolution based on the Dirac delta function to simplify the analysis of motion invariant in frequency and moment domain. This model paves the way for estimating the motion angle and length in terms of the proposed invariant features. In this research, the performance of the proposed schemes is compared with other state-of-the-art existing methods of image deblurring. The experimental study performs using fetal phantom images and clinical fetal ultrasound images as well as breast scans. Moreover, to validate the accuracy of the proposed experimental framework, we apply two image quality assessment methods as no-reference and full-reference to show the robustness of the proposed algorithms compared to the well-known approaches. WOS JD 20000 20200 20205 2022 3 RVO:67985556 http://hdl.handle.net/11104/0324805 1 S 14312 3+4 Article Multidisciplinary Sciences A MULTIDISCIPLINARYSCIENCES 5.516 0.973 4.2 1.17627 1.208 696335 347 1.005 47.09 4.67 289850 Q1 4.784 23363 Q2 75 Q2 Q2 1.05 Q1 75 2021 85110438127 SCOPUS PUBMED 000677493500009 WOS cav_un_epca*0386594 Scientific Reports 2045-2322 2045-2322 Roč. 11 č. 1 2021 Nature Publishing Group