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.

A Motion Blurred Image Restoration Method Based on Wiener Filtering

2011 ◽  
Vol 403-408 ◽  
pp. 1664-1667 ◽  
Author(s):  
Qian Qian Quan
Keyword(s):  
Image Restoration ◽  
Motion Blur ◽  
Experimental Results ◽  
Wiener Filtering ◽  
Degradation Model ◽  
Image Blurring ◽  
Blurred Image ◽  
Working Principle ◽  
Restoration Method ◽  
Blurred Images

To the deficiencies of traditional methods for avoiding motion image blurring, a motion blur image restoration method is studied based on Wiener filtering in this paper. The formation factors of motion-blurred images and the imaging process are analyzed, and the motion blur degradation model is established. It introduced the working principle of Wiener filtering, described the steps of blurred image restoration in details. The experiment testing and data analyzing are also conducted. Experimental results showed that the method can has good performance.

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.

A Research on the Optimization of Fuzzy Image

2013 ◽  
Vol 409-410 ◽  
pp. 1593-1596
Author(s):  
Xue Feng Wu ◽  
Yu Fan
Keyword(s):  
Mathematical Model ◽  
Image Restoration ◽  
Point Spread Function ◽  
Motion Blur ◽  
Inverse Filtering ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Blurred Images ◽  
Fuzzy Image

The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given According to the characteristics of blurred images the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration, Lucy-Richardson image restoration and Wiener image restoration. The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved, and the image restoration is more stable.

Parameter Estimation and Restoration of Motion-Blurred Images

2012 ◽  
Vol 239-240 ◽  
pp. 1138-1141
Author(s):  
Pei Zhi Wen ◽  
Kai Guo ◽  
Li Fang Li
Keyword(s):  
Image Processing ◽  
Fourier Transform ◽  
Parameter Estimation ◽  
Image Restoration ◽  
Estimation Method ◽  
Parameter Estimation Method ◽  
Length Estimation ◽  
Blurred Image ◽  
Blurred Images

The motion-blurred image restoration has been the difficulty of the field of image processing. In this paper, proposed a motion-blurred length method based on Fourier transform of the parameter estimation method. The method improved the accuracy of the blurred length estimation. The method improved the effect of the motion-blurred image restoration. The experiments verified that the accuracy and feasibility of the method proposed.

Parameter Estimation and Restoration of Motion Blurred Image

2014 ◽  
Vol 608-609 ◽  
pp. 855-859 ◽  
Author(s):  
Yu Xiang Song ◽  
Yan Mei Zhang
Keyword(s):  
Parameter Estimation ◽  
Image Restoration ◽  
Rotation Axis ◽  
Image Features ◽  
Motion Blur ◽  
Binary Image ◽  
Degraded Image ◽  
Image Center ◽  
Blurred Image ◽  
The Difference

according to the real motion blur image restoration problems, analyze the difference between the image features and Simulation of real blurred images, this paper proposes a method that applied to real image degradation parameter estimation. First calculate the degraded image using cepstrum, taking the cepstrum to binary image using absolute value of minimum gray as the threshold, and then remove the center cross bright line; and then use formula of point to line to calculate the distance of bright fringe direction of binary image, that is direction of motion blur; the direction of motion blur is rotated to the horizontal direction by the degraded image center of rotation axis, divided the autocorrelation method to calculate fuzzy scale. To estimate the point spread function is take into the Wiener filtering algorithm to recover images, image restoration effect prove that parameter estimation results are correct.

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 Cameras and settings for aerial surveys in the geosciences

2017 ◽  
Vol 41 (3) ◽  
pp. 325-344 ◽  
Author(s):  
James O’Connor ◽  
Mike J Smith ◽  
Mike R James
Keyword(s):  
Image Quality ◽  
Image Data ◽  
Worked Examples ◽  
Motion Blur ◽  
Aerial Image ◽  
Primary Data ◽  
Target Feature ◽  
Image Capture ◽  
Capture Process ◽  
Ground Sample

Aerial image capture has become very common within the geosciences due to the increasing affordability of low-payload (<20 kg) unmanned aerial vehicles (UAVs) for consumer markets. Their application to surveying has subsequently led to many studies being undertaken using UAV imagery and derived products as primary data sources. However, image quality and the principles of image capture are seldom given rigorous discussion. In this contribution we firstly revisit the underpinning concepts behind image capture, from which the requirements for acquiring sharp, well-exposed and suitable image data are derived. Secondly, the platform, camera, lens and imaging settings relevant to image quality planning are discussed, with worked examples to guide users through the process of considering the factors required for capturing high-quality imagery for geoscience investigations. Given a target feature size and ground sample distance based on mission objectives, the flight height and velocity should be calculated to ensure motion blur is kept to a minimum. We recommend using a camera with as large a sensor as is permissible for the aerial platform being used (to maximise sensor sensitivity), effective focal lengths of 24–35 mm (to minimise errors due to lens distortion) and optimising ISO (to ensure the shutter speed is fast enough to minimise motion blur). Finally, we give recommendations for the reporting of results by researchers in order to help improve the confidence in, and reusability of, surveys through providing open access imagery where possible, presenting example images and excerpts and detailing appropriate metadata to rigorously describe the image capture process.

Study on Motion Blur Image Restoration Algorithms

2013 ◽  
Vol 753-755 ◽  
pp. 2976-2979
Author(s):  
Yu Fan ◽  
Xue Feng Wu
Keyword(s):  
Mathematical Model ◽  
Image Restoration ◽  
Point Spread Function ◽  
Motion Blur ◽  
Inverse Filtering ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Restoration Algorithms ◽  
Blurred Images

The restore algorithm of the image blurred by motion is proposed, and a mathematical model based on motion blur system is eomtrueted£®The Point spread function of the motion blur is given£®According to the characteristics of blurred images£¬the parameters of point spread function are estimated ,and three methods are introduced for image restoration. The three methods are inverse filtering of image restoration,Lucy-Richardson image restoration and Wiener image restoration.The principles of the three image restoration methods are analyzed. The motion blurred image restoration experiment is made. The results show that the visibility of the image is improved ,and the image restoration is more stable.

Aerial Transmission Lines Image Restoration Technology Research Based on the Improved Maximum Entropy

2013 ◽  
Vol 401-403 ◽  
pp. 1315-1318
Author(s):  
Bao Shu Li ◽  
Wen Li Wei ◽  
Ke Bin Cui ◽  
Xue Tao Xu
Keyword(s):  
Image Restoration ◽  
Maximum Entropy ◽  
Transmission Lines ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
First Order ◽  
Image Blurring ◽  
Blurred Image ◽  
Restoration Method

According to the limitations of the shooting environment, captured image exist the phenomenon of image blurring and noise. This paper proposes that the improved maximum entropy method recovery blurred image which acquire in aerial. Finally, according to the first order Markoff theory to evaluate the quality of the processed image, the results show that maximum entropy image restoration method compared to the conventional approach increase image clarity and details more better.

Sign in / Sign up

Export Citation Format

Share Document