Improved scheme of estimating motion blur parameters for image restoration

Optimization is the process that relates to finding the most excellent ways for all possible solutions. From last 2-3 decades, natural algorithms play an important role in improving solutions of various problems. By comparing various meta-heuristic algorithms, researchers can make a choice to the best selection of the meta-heuristic algorithms for the proposed problem. In this particular research, we have applied New Cepstrum based technique of image restoration to find out PSF parameters of motion blurred images as a primary technique. In addition, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), BAT Algorithm and GA-BAT hybrid technique etc. are also applied to optimize the blur parameters for calculated by new cepstrum based technique for blur estimation. This aids in analyzing the performance of each algorithm on the same primary technique. The performance analysis of all four algorithms aid in making the decision on the best meta-heuristic algorithm of the cepstrum based technique and to identify the preciseness of the motion blur. All four methods are applied to the same set of images. The algorithm is tested and compared using grayscale images and the benchmarking freely available online datasets, respectivel

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Image Restoration Model Using Total Variance, Bilateral and Wavelet Denoising Filter

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4731.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 5888-5891

Keyword(s):

Image Restoration ◽

Bilateral Filter ◽

Image Transmission ◽

Motion Blur ◽

Total Variance ◽

Atmospheric Conditions ◽

Image Degradation ◽

Variance Filter ◽

Degraded Image ◽

Wavelet Image

Noise in images are most common due to various degradation. Noises in images are random variations in images due to lighting conditions, camera electronics, surface reflectance, lens, atmospheric conditions and motions (Either camera is moving or object is moving). Image Restoration is a process which restores a degraded image into its original image which has been degraded by some degradation model which degraded the image. Images are degraded due to various reasons. The first and foremost reason for image degradation is the fault in the imaging devices during the image acquisition process. The noise is generated in the imaging devices and is propagated to the image. The second source of degradation in image is the noise added during the image transmission. This type of image degradation is most common. The third source of image degradation is due to the motion blur and atmospheric turbulence. This paper analyzes various image noise models and restoration techniques. Particularly in analyses three kind of filters namely total variance filter, bilateral filter and wavelet image denoising. The image restoration is measured using the PSNR and SSI of original and degraded images

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PSO Based Blind Deconvolution Technique of Image Restoration Using Cepstrum Domain of Motion Blur

Computational Vision and Bio Inspired Computing - Lecture Notes in Computational Vision and Biomechanics ◽

10.1007/978-3-319-71767-8_81 ◽

2018 ◽

pp. 947-958 ◽

Cited By ~ 1

Author(s):

G. Ramteke Mamta ◽

Maitreyee Dutta

Keyword(s):

Image Restoration ◽

Blind Deconvolution ◽

Motion Blur ◽

Deconvolution Technique

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A Research on the Optimization of Fuzzy Image

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.409-410.1593 ◽

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.

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Parameter Estimation and Restoration of Motion Blurred Image

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.608-609.855 ◽

2014 ◽

Vol 608-609 ◽

pp. 855-859 ◽

Cited By ~ 1

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.

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