Restoration of out of focus images using neural network

Chen, Hun-Chen; Yen, Jui-Cheng; Chen, Hung‐Chun

doi:10.1109/isic.2012.6449747

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…However, this method is noise independent, but it requires manually adjustment of some parameters. Some other methods presented in (Jiang,Wu, Guo, 2005;Su, Li, Xu,et al, 2008;Chen and Yen, 2012)have used wavelet coefficients as features to train and test the radial basis function or cellular neural network for parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation for Defocus Blurred Image Based on Polar Transformation

2016

Rev. Téc. Ing. Univ. Zulia.

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The accurate estimation scheme of point spread function (PSF) is very important for obtaining good restoration performance in image restoration. For the unknown blur parameter of a single defocus image, an estimation method is proposed. First, polar transformation is applied to the logarithmic power spectrum of the defocus image in the frequency domain. Then the defocus blur radius can be obtained by calculating the sum of the logarithmic spectrum along concentric circles in the polar coordinate. The experiment results show that for noise free blurred images, the method can achieve more accurate estimation of the point spread function within the blur radius range of 0 55 R   pixels; for Gaussian noise blurred images, the restoration of blurred and noisy images demonstrates good performance.

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Section: Introductionmentioning

confidence: 99%

Parameter Estimation for Defocus Blurred Image Based on Polar Transformation

2016

Rev. Téc. Ing. Univ. Zulia.

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“…However, this method is noise independent, but it requires manually adjustment of some parameters. Some other methods presented in [16][17][18][19] have used wavelet coefficients as features to train and test the radial basis function (RBF) or cellular neural network for parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

Blur parameters identification for simultaneous defocus and motion blur

Tiwari

Shukla

Biradar

et al. 2014

CSIT

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Motion blur and defocus blur are common cause of image degradation. Blind restoration of such images demands identification of the accurate point spread function for these blurs. The identification of joint blur parameters in barcode images is considered in this paper using logarithmic power spectrum analysis. First, Radon transform is utilized to identify motion blur angle. Then we estimate the motion blur length and defocus blur radius of the joint blurred image with generalized regression neural network (GRNN). The input of GRNN is the sum of the amplitudes of the normalized logarithmic power spectrum along vertical direction and concentric circles for motion and defocus blurs respectively. This scheme is tested on multiple barcode images with varying parameters of joint blur. We have also analyzed the effect of joint blur when one blur has same, greater or lesser extents to another one. The results of simulation experiments show the high precision of proposed method and reveals that dominance of one blur on another does not affect too much on the applied parameter estimation approach.

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Defocus blur radius classification using random forest classifier

Gajjar

Zaveri

2017

2017 International Conference on Innovations in Electronics, Signal Processing and Communication (IESC)

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Restoration of out of focus images using neural network

Cited by 5 publications

References 11 publications

Parameter Estimation for Defocus Blurred Image Based on Polar Transformation

Parameter Estimation for Defocus Blurred Image Based on Polar Transformation

Blur parameters identification for simultaneous defocus and motion blur

Defocus blur radius classification using random forest classifier

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