Motion blurred image blind restoration base on parameter estimation of PSF

Use of a mobile camera for barcode decoding provides high portability and availability but it requires that the recorded barcode image must be accurate representation of the barcode that is available on the product. Barcode scanning is challenging because images may be degraded due to out-of-focus blur at the time of image acquisition. Therefore, image restoration is essential in making image sharp and useful. In case of blind restoration of such barcode images accurate estimation of out-of-focus blur parameter is highly desirable. In this article, a robust method has been proposed for estimating the radius of out-of-focus blur. Finite discrete ridgelet transform has been used to find the features of the blurred image and a radial basis function neural network is utilized to estimate the radius of out-of-focus blur. The experimental results reveal that proposed method more robust than the existing methods.

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Parameter Estimation and Restoration of Motion-Blurred Images

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.239-240.1138 ◽

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.

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Research on the blind restoration algorithm of motion-blurred image

2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) ◽

10.1109/imcec.2016.7867241 ◽

2016 ◽

Author(s):

Yan Ge

Keyword(s):

Restoration Algorithm ◽

Blurred Image ◽

Blind Restoration

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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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No-Reference Digital Image Quality Assessment Based on Structure Similarity

10.3233/faia210156 ◽

2021 ◽

Author(s):

Basma Ahmed ◽

Mohamed Abdel-Nasser ◽

Osama A. Omer ◽

Amal Rashed ◽

Domenec Puig

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Similarity Index ◽

Structural Similarity ◽

Structural Similarity Index ◽

Spectral Kurtosis ◽

Blurred Image ◽

Blind Restoration ◽

Index Measure

Blind or non-referential image quality assessment (NR-IQA) indicates the problem of evaluating the visual quality of an image without any reference, Therefore, the need to develop a new measure that does not depend on the reference pristine image. This paper presents a NR-IQA method based on restoration scheme and a structural similarity index measure (SSIM). Specifically, we use blind restoration schemes for blurred images by reblurring the blurred image and then we use it as a reference image. Finally, we use the SSIM as a full reference metric. The experiments performed on standard test images as well as medical images. The results demonstrated that our results using a structural similarity index measure are better than other methods such as spectral kurtosis-based method.

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