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 Design a model of Image Restoration using AI in Digital Image Processing

2021 ◽  
Vol 12 (5) ◽  
pp. 862-865
Author(s):  
Boosi Shyamala, Dr. Chetana Tukkoji, Archana S Nadhan, Dioline Sara
Keyword(s):  
Image Processing ◽  
Image Restoration ◽  
Digital Image Processing ◽  
Digital Image ◽  
Motion Blur ◽  
Original Image ◽  
Source Imaging ◽  
Point Extension ◽  
Noise Image ◽  
Blurred Image

Image restoration is the process of obtaining a distorted/noise image and giving an approximate clear image of the original image. False focus, motion blur and noise are forms of distortion. Image restoration can be done by reversing the process called Point Extension Function (PSF). In this process, the blurred image is generated by point source imaging and can be used to restore the image lost due to the blur process. Like to form. Modern artificial intelligence (AI) applied to image processing includes facial recognition, object recognition and detection, video, image action, and visual search. It helps to develop smart applications in digital image processing.

scholarly journals Aplikasi Perbandingan Sistem Perbaikan Citra Digital menggunakan Metode Dekonvolusi Wiener, Lucy Richardson, dan Regularized

2020 ◽  
Vol 4 (2) ◽  
pp. 116
Author(s):  
Dika Rizki Darmawan ◽  
Fauziah Fauziah ◽  
Ratih Titi Komalasari
Keyword(s):  
Image Processing ◽  
Image Restoration ◽  
Motion Blur ◽  
Test Image ◽  
Image Capture ◽  
Capture Process ◽  
Image Blurring ◽  
Blurred Image ◽  
Image Improvement ◽  
Different Levels

In some cases, there is some damage to an image caused by interference during the image capture process. Blurred image damage can be overcome by deconvolution digital image processing. There are various methods to repair the image blur damage, including using the Regularized, Wiener, and Lucy Richardson deconvolution methods. Each blurring image repair method produces a different debluring result of image processing. Image comparison application was built to compare the ability of image restoration results to a Motion Blur image with the algorithms used in deconvolution. Image restoration comparison parameters used include determining the MSE and PSNR values between the test image and the deconvolved image. The results of implementing the comparative application of Motion Blur image improvement to 270 blur simulations consisting of 9 different levels of image blurring, obtained the average PSNR value for Wiener's deconvolution = 59.16dB, Lucy Richardson = 26.92dB and Regularized = 36.94dB.Keywords:Image Restoration; Lucy Richardson; Motion Blur; Regularized; Wiener.

Velocity Estimation From a Single Linear Motion Blurred Image Using Discrete Cosine Transform

2019 ◽  
Vol 68 (10) ◽  
pp. 4038-4050 ◽  
Author(s):  
Jimy Alexander Cortes-Osorio ◽  
Juan Bernardo Gomez-Mendoza ◽  
Juan Carlos Riano-Rojas
Keyword(s):  
Discrete Cosine Transform ◽  
Velocity Estimation ◽  
Linear Motion ◽  
Cosine Transform ◽  
Blurred Image

scholarly journals Fast Motion Deblurring Using Sensor-Aided Motion Trajectory Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Eunsung Lee ◽  
Eunjung Chae ◽  
Hejin Cheong ◽  
Joonki Paik
Keyword(s):  
Inertial Sensors ◽  
Motion Blur ◽  
Trajectory Estimation ◽  
Motion Trajectories ◽  
Motion Deblurring ◽  
Spread Function ◽  
Blurred Image ◽  
Fast Motion ◽  
Noise Amplification ◽  
Spatially Varying

This paper presents an image deblurring algorithm to remove motion blur using analysis of motion trajectories and local statistics based on inertial sensors. The proposed method estimates a point-spread-function (PSF) of motion blur by accumulating reweighted projections of the trajectory. A motion blurred image is then adaptively restored using the estimated PSF and spatially varying activity map to reduce both restoration artifacts and noise amplification. Experimental results demonstrate that the proposed method outperforms existing PSF estimation-based motion deconvolution methods in the sense of both objective and subjective performance measures. The proposed algorithm can be employed in various imaging devices because of its efficient implementation without an iterative computational structure.

Validity of Definition Assessments for Motion Blur Identification

2011 ◽  
Vol 474-476 ◽  
pp. 1578-1582
Author(s):  
Shou Bing Xiang ◽  
Jin Ping He ◽  
Guang Da Su
Keyword(s):  
Experimental Data ◽  
Motion Blur ◽  
Linear Motion ◽  
Variable Parameters ◽  
Blur Identification ◽  
Image Definition

In this paper, based on the series of test images which are synthesized through the linear motion blur model with continue variable parameters, we define the sensitivity of image definition assessments. Experimental data are presented that the validity of definition assessments for motion blur identification closely relates to the sensitivity of definition assessments. The higher the sensitivity is, the better the validity of definition assessments for identification performs.

scholarly journals Texture Enhancement Based on the Savitzky-Golay Fractional Differential Operator

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Hamid A. Jalab ◽  
Rabha W. Ibrahim
Keyword(s):  
Image Processing ◽  
Differential Operator ◽  
Fractional Order ◽  
Fractional Operators ◽  
Enhancement Technique ◽  
Fractional Differential Operator ◽  
Weight Window ◽  
Fractional Differential ◽  
Fractional Filter ◽  
The Given

Texture enhancement for digital images is the most important technique in image processing. The purpose of this paper is to design a texture enhancement technique using fractional order Savitzky-Golay differentiator, which leads to generalizing the Savitzky-Golay filter in the sense of the Srivastava-Owa fractional operators. By employing this generalized fractional filter, texture enhancement is introduced. Consequently, it calculates the generalized fractional order derivative of the given image using the sliding weight window over the image. Experimental results show that the operator can extract more subtle information and make the edges more prominent. In general, the capability of the generalized fractional differential will be high because it is sensitive to the subtle fluctuations of values of pixels.

Design of Assistance System Based on OpenCV for Color-Blindness

2013 ◽  
Vol 321-324 ◽  
pp. 1098-1101
Author(s):  
Zhan Rong Feng ◽  
Li Xia Wang ◽  
Gang Yao Zhao
Keyword(s):  
Image Processing ◽  
Continuous Improvement ◽  
Color Space ◽  
Color Discrimination ◽  
Color Blindness ◽  
Assistance System ◽  
Rotation Transformation ◽  
Discrimination Capacity ◽  
The Given

With the wide application of image processing as well as continuous improvement of processing means, carrying out rotation transformation of H component of the color space with OpenCV can improve the resolution for color and make up for the color discrimination capacity of the achromat effectively. Experiments have shown that when the type of color-blindness is given and the colors of the image are confusing for the given achromat, rotate the H component by 105~130°, the achromat can identify the colors well.

scholarly journals Monitoring Dynamic Structural Tests Using Image Deblurring Techniques

2013 ◽  
Vol 569-570 ◽  
pp. 932-939 ◽  
Author(s):  
David M.J. McCarthy ◽  
Jim H. Chandler ◽  
Alessandro Palmeri
Keyword(s):  
Image Processing ◽  
Real Time ◽  
High Speed ◽  
Image Resolution ◽  
Test Element ◽  
Small Scale ◽  
Measuring Instruments ◽  
Imaging Results ◽  
Blurred Image ◽  
Structural Tests

Photogrammetric techniques have demonstrated their suitability for monitoring static structural tests. Advantages include scalability, reduced cost, and three dimensional monitoring of very high numbers of points without direct contact with the test element. Commercial measuring instruments now exist which use this approach. Dynamic testing is becoming a convenient approach for long-term structural health monitoring. If image based methods could be applied to the dynamic case, then the above advantages could prove beneficial. Past work has been successful where the vibration has either large amplitude or low frequency, as even specialist imaging sensors are limited by an inherent compromise between image resolution and imaging frequency. Judgement in sensor selection is therefore critical. Monitoring of structures in real-time is possible only at a reduced resolution, and although imaging and computer processing hardware continuously improves, so the accuracy demands of researchers and engineers increase. A new approach to measuring vibration is introduced here, whereby a long-exposure photograph is used to capture a blurred image of the vibrating structure. The high resolution blurred image showing the whole vibration interval is measured with no need for high-speed imaging. Results are presented for a series of small-scale laboratory models, as well as a larger scale test, which demonstrate the flexibility of the proposed technique. Different image processing strategies are presented and compared, as well as the effects of exposure, aperture and sensitivity selection. Image processing time appears much faster, increasing suitability for real-time monitoring.

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