Effective Pretreatment in Identification of Motion-Blur Direction

2011 ◽  
Author(s):  
Xia Yubin ◽  
Meng Fanbin
Keyword(s):  
Motion Blur ◽  
Blur Direction

scholarly journals Using a Blur Metric to Estimate Linear Motion Blur Parameters

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Taiebeh Askari Javaran ◽  
Hamid Hassanpour
Keyword(s):  
Image Processing ◽  
Health Services ◽  
Motion Blur ◽  
Linear Motion ◽  
Sensitive Stage ◽  
Blurred Image ◽  
The Given ◽  
Blur Direction ◽  
Sharp Version ◽  
Monotonic Relation

Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blurry, this method can be used to estimate the blur parameters and then take an action to enhance the image. In this paper, some methods are proposed for estimating the linear motion blur parameters based on the extraction of features from the given single blurred image. The motion blur direction is estimated using the Radon transform of the spectrum of the blurred image. To estimate the motion blur length, the relation between a blur metric, called NIDCT (Noise-Immune Discrete Cosine Transform-based), and the motion blur length is applied. Experiments performed in this study showed that the NIDCT blur metric and the blur length have a monotonic relation. Indeed, an increase in blur length leads to increase in the blurriness value estimated via the NIDCT blur metric. This relation is applied to estimate the motion blur. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.

PSF Estimation and Image Restoration for Motion Blurred Images

2012 ◽  
Vol 562-564 ◽  
pp. 2124-2127 ◽  
Author(s):  
Qi Shen Li ◽  
Jian Gong Chen
Keyword(s):  
Fourier Transform ◽  
Image Restoration ◽  
Signal To Noise Ratio ◽  
Motion Blur ◽  
Blurred Image ◽  
The Fourier Transform ◽  
Original Motion ◽  
Blurred Images ◽  
Blur Direction ◽  
Psf Estimation

Point spread function (PSF) estimation and image restoration algorithm are the hotspots In the research of motion blurred image restoration. In order to improve the efficacy of image restoration, an improved algorithm named quadric transforms (QT) method is proposed in this paper by analyzing the restoration process of motion blurred images. Firstly, Fourier transform and homomorphism transform are applied to the original motion blurred image, and then the Fourier transform and homomorphism transform are used again to the obtained spectrum image. Secondly, the motion blur direction is estimated by Radon transform. Thirdly, the motion blur length is found by differential autocorrelation operations. Finally, utilizing the estimated blur direction and blur length, the motion blurred image is restored by Wiener filtering. Experimental results show that the proposed QT method can get more accurate estimated motion blur angles than the primary transform (PT, that is, Fourier transform and homomorphism transform are used one time) method and can get better restored images under the meaning of peak signal to noise ratio (PSNR).

scholarly journals Motion blur invariant for estimating motion parameters of medical ultrasound images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Barmak Honarvar Shakibaei Asli ◽  
Yifan Zhao ◽  
John Ahmet Erkoyuncu
Keyword(s):  
Image Quality Assessment ◽  
Medical Image Analysis ◽  
Main Idea ◽  
Delta Function ◽  
Motion Blur ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Fetal Ultrasound ◽  
Dirac Delta ◽  
Motion Parameters

AbstractHigh-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.

scholarly journals Tracking by Deblatting

2021 ◽  
Author(s):  
Denys Rozumnyi ◽  
Jan Kotera ◽  
Filip Šroubek ◽  
Jiří Matas
Keyword(s):  
High Speed ◽  
High Performance ◽  
Motion Blur ◽  
Velocity Estimation ◽  
High Speed Camera ◽  
Trajectory Function ◽  
Novel Approach ◽  
Blind Deblurring ◽  
Object Trajectories ◽  
Complex Trajectories

AbstractObjects moving at high speed along complex trajectories often appear in videos, especially videos of sports. Such objects travel a considerable distance during exposure time of a single frame, and therefore, their position in the frame is not well defined. They appear as semi-transparent streaks due to the motion blur and cannot be reliably tracked by general trackers. We propose a novel approach called Tracking by Deblatting based on the observation that motion blur is directly related to the intra-frame trajectory of an object. Blur is estimated by solving two intertwined inverse problems, blind deblurring and image matting, which we call deblatting. By postprocessing, non-causal Tracking by Deblatting estimates continuous, complete, and accurate object trajectories for the whole sequence. Tracked objects are precisely localized with higher temporal resolution than by conventional trackers. Energy minimization by dynamic programming is used to detect abrupt changes of motion, called bounces. High-order polynomials are then fitted to smooth trajectory segments between bounces. The output is a continuous trajectory function that assigns location for every real-valued time stamp from zero to the number of frames. The proposed algorithm was evaluated on a newly created dataset of videos from a high-speed camera using a novel Trajectory-IoU metric that generalizes the traditional Intersection over Union and measures the accuracy of the intra-frame trajectory. The proposed method outperforms the baselines both in recall and trajectory accuracy. Additionally, we show that from the trajectory function precise physical calculations are possible, such as radius, gravity, and sub-frame object velocity. Velocity estimation is compared to the high-speed camera measurements and radars. Results show high performance of the proposed method in terms of Trajectory-IoU, recall, and velocity estimation.

scholarly journals Improved in vivo imaging method for individual islets across the mouse pancreas reveals a heterogeneous insulin secretion response to glucose

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Henriette Frikke-Schmidt ◽  
Peter Arvan ◽  
Randy J. Seeley ◽  
Corentin Cras-Méneur
Keyword(s):  
Insulin Secretion ◽  
In Vivo Imaging ◽  
Motion Blur ◽  
Isolated Islets ◽  
Imaging Method ◽  
Glucose Stimulation ◽  
Powerful Approach ◽  
Glucose Challenge ◽  
The Impact

AbstractWhile numerous techniques can be used to measure and analyze insulin secretion in isolated islets in culture, assessments of insulin secretion in vivo are typically indirect and only semiquantitative. The CpepSfGFP reporter mouse line allows the in vivo imaging of insulin secretion from individual islets after a glucose stimulation, in live, anesthetized mice. Imaging the whole pancreas at high resolution in live mice to track the response of each individual islet over time includes numerous technical challenges and previous reports were only limited in scope and non-quantitative. Elaborating on this previous model—through the development of an improved methodology addressing anesthesia, temperature control and motion blur—we were able to track and quantify longitudinally insulin content throughout a glucose challenge in up to two hundred individual islets simultaneously. Through this approach we demonstrate quantitatively for the first time that while isolated islets respond homogeneously to glucose in culture, their profiles differ significantly in vivo. Independent of size or location, some islets respond sharply to a glucose stimulation while others barely secrete at all. This platform therefore provides a powerful approach to study the impact of disease, diet, surgery or pharmacological treatments on insulin secretion in the intact pancreas in vivo.

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