Parameter Estimation and Restoration for Motion Blurred Images

Qiang LI; Yasuo YOSHIDA

Parameter Estimation and Restoration for Motion Blurred Images

Qiang LI, Yasuo YOSHIDA

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The parameter estimation problem of point spread function is one of the most challenging and important task for image restoration. A new method for the parameter estimation in the case of motion blur is presented here. It is based on the principle that the power spectrum of the motion blurred image contains periodical minima relevant directly to the motion derection and length. Though the principle is very simple and effective in certain cases, the direct use of it may lead to poor performance an the signal-to-noise ratio (SNR) gets lower. To improve the estimation accuracy, by analyzing image noise effect on the detection of the minima, we propose a method to greatly reduce spectral noise, and give the lowest allowed SNR at which the minima may still be identified reliably. We also estimate the power spectrum of the original image, which is a must for the Wiener restoration filter, from the noisy blurred image based on a noncasual autoregressive model. Once above parameters are decided, the Wiener filter is used to restore the noisy blurred image. Our method is very practical; no parameter needs to be known a priori, or to be adjusted manually to fit into various application problems. The proposed method is finally applied to systhesized and real motion blurred images to demonstrate its effectiveness.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E80-A No.8 pp.1430-1437

Publication Date: 1997/08/25

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Type of Manuscript: Special Section PAPER (Special Section on Digital Signal Processing)

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Qiang LI, Yasuo YOSHIDA, "Parameter Estimation and Restoration for Motion Blurred Images" in IEICE TRANSACTIONS on Fundamentals, vol. E80-A, no. 8, pp. 1430-1437, August 1997, doi: .
Abstract: The parameter estimation problem of point spread function is one of the most challenging and important task for image restoration. A new method for the parameter estimation in the case of motion blur is presented here. It is based on the principle that the power spectrum of the motion blurred image contains periodical minima relevant directly to the motion derection and length. Though the principle is very simple and effective in certain cases, the direct use of it may lead to poor performance an the signal-to-noise ratio (SNR) gets lower. To improve the estimation accuracy, by analyzing image noise effect on the detection of the minima, we propose a method to greatly reduce spectral noise, and give the lowest allowed SNR at which the minima may still be identified reliably. We also estimate the power spectrum of the original image, which is a must for the Wiener restoration filter, from the noisy blurred image based on a noncasual autoregressive model. Once above parameters are decided, the Wiener filter is used to restore the noisy blurred image. Our method is very practical; no parameter needs to be known a priori, or to be adjusted manually to fit into various application problems. The proposed method is finally applied to systhesized and real motion blurred images to demonstrate its effectiveness.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e80-a_8_1430/_p

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@ARTICLE{e80-a_8_1430,
author={Qiang LI, Yasuo YOSHIDA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Parameter Estimation and Restoration for Motion Blurred Images},
year={1997},
volume={E80-A},
number={8},
pages={1430-1437},
abstract={The parameter estimation problem of point spread function is one of the most challenging and important task for image restoration. A new method for the parameter estimation in the case of motion blur is presented here. It is based on the principle that the power spectrum of the motion blurred image contains periodical minima relevant directly to the motion derection and length. Though the principle is very simple and effective in certain cases, the direct use of it may lead to poor performance an the signal-to-noise ratio (SNR) gets lower. To improve the estimation accuracy, by analyzing image noise effect on the detection of the minima, we propose a method to greatly reduce spectral noise, and give the lowest allowed SNR at which the minima may still be identified reliably. We also estimate the power spectrum of the original image, which is a must for the Wiener restoration filter, from the noisy blurred image based on a noncasual autoregressive model. Once above parameters are decided, the Wiener filter is used to restore the noisy blurred image. Our method is very practical; no parameter needs to be known a priori, or to be adjusted manually to fit into various application problems. The proposed method is finally applied to systhesized and real motion blurred images to demonstrate its effectiveness.},
keywords={},
doi={},
ISSN={},
month={August},}

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TY - JOUR
TI - Parameter Estimation and Restoration for Motion Blurred Images
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1430
EP - 1437
AU - Qiang LI
AU - Yasuo YOSHIDA
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E80-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1997
AB - The parameter estimation problem of point spread function is one of the most challenging and important task for image restoration. A new method for the parameter estimation in the case of motion blur is presented here. It is based on the principle that the power spectrum of the motion blurred image contains periodical minima relevant directly to the motion derection and length. Though the principle is very simple and effective in certain cases, the direct use of it may lead to poor performance an the signal-to-noise ratio (SNR) gets lower. To improve the estimation accuracy, by analyzing image noise effect on the detection of the minima, we propose a method to greatly reduce spectral noise, and give the lowest allowed SNR at which the minima may still be identified reliably. We also estimate the power spectrum of the original image, which is a must for the Wiener restoration filter, from the noisy blurred image based on a noncasual autoregressive model. Once above parameters are decided, the Wiener filter is used to restore the noisy blurred image. Our method is very practical; no parameter needs to be known a priori, or to be adjusted manually to fit into various application problems. The proposed method is finally applied to systhesized and real motion blurred images to demonstrate its effectiveness.
ER -