IEICE TRANSACTIONS on Information
Blind Image Identification and Restoration for Noisy Blurred Images Based on Discrete Sine Transform
Summary :
This paper presents a maximum likelihood (ML) identification and restoration method for noisy blurred images. The unitary discrete sine transform (DST) is employed to decouple the large order spatial state-space representation of the noisy blurred image into a bank of one-dimensional real state-space scalar subsystems. By assuming that the noises are Gaussian distributed processes, the maximum likelihood estimation technique using the expectation-maximization (EM) algorithm is developed to jointly identify the blurring functions, the image model parameters and the noise variances. In order to improve the computational efficiency, the conventional Kalman smoother is incorporated to give the estimates. The identification process also yields the estimates of transformed image data, from which the original image is restored by the inverse DST. The experimental results show the effectiveness of the proposed method and its superiority over the recently proposed spatial domain DFT-based methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E86-D No.4 pp.727-735
- Publication Date
- 2003/04/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Image Processing, Image Pattern Recognition
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Dongliang HUANG, Naoyuki FUJIYAMA, Sueo SUGIMOTO, "Blind Image Identification and Restoration for Noisy Blurred Images Based on Discrete Sine Transform" in IEICE TRANSACTIONS on Information,
vol. E86-D, no. 4, pp. 727-735, April 2003, doi: .
Abstract: This paper presents a maximum likelihood (ML) identification and restoration method for noisy blurred images. The unitary discrete sine transform (DST) is employed to decouple the large order spatial state-space representation of the noisy blurred image into a bank of one-dimensional real state-space scalar subsystems. By assuming that the noises are Gaussian distributed processes, the maximum likelihood estimation technique using the expectation-maximization (EM) algorithm is developed to jointly identify the blurring functions, the image model parameters and the noise variances. In order to improve the computational efficiency, the conventional Kalman smoother is incorporated to give the estimates. The identification process also yields the estimates of transformed image data, from which the original image is restored by the inverse DST. The experimental results show the effectiveness of the proposed method and its superiority over the recently proposed spatial domain DFT-based methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_4_727/_p
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@ARTICLE{e86-d_4_727,
author={Dongliang HUANG, Naoyuki FUJIYAMA, Sueo SUGIMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Blind Image Identification and Restoration for Noisy Blurred Images Based on Discrete Sine Transform},
year={2003},
volume={E86-D},
number={4},
pages={727-735},
abstract={This paper presents a maximum likelihood (ML) identification and restoration method for noisy blurred images. The unitary discrete sine transform (DST) is employed to decouple the large order spatial state-space representation of the noisy blurred image into a bank of one-dimensional real state-space scalar subsystems. By assuming that the noises are Gaussian distributed processes, the maximum likelihood estimation technique using the expectation-maximization (EM) algorithm is developed to jointly identify the blurring functions, the image model parameters and the noise variances. In order to improve the computational efficiency, the conventional Kalman smoother is incorporated to give the estimates. The identification process also yields the estimates of transformed image data, from which the original image is restored by the inverse DST. The experimental results show the effectiveness of the proposed method and its superiority over the recently proposed spatial domain DFT-based methods.},
keywords={},
doi={},
ISSN={},
month={April},}
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TY - JOUR
TI - Blind Image Identification and Restoration for Noisy Blurred Images Based on Discrete Sine Transform
T2 - IEICE TRANSACTIONS on Information
SP - 727
EP - 735
AU - Dongliang HUANG
AU - Naoyuki FUJIYAMA
AU - Sueo SUGIMOTO
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2003
AB - This paper presents a maximum likelihood (ML) identification and restoration method for noisy blurred images. The unitary discrete sine transform (DST) is employed to decouple the large order spatial state-space representation of the noisy blurred image into a bank of one-dimensional real state-space scalar subsystems. By assuming that the noises are Gaussian distributed processes, the maximum likelihood estimation technique using the expectation-maximization (EM) algorithm is developed to jointly identify the blurring functions, the image model parameters and the noise variances. In order to improve the computational efficiency, the conventional Kalman smoother is incorporated to give the estimates. The identification process also yields the estimates of transformed image data, from which the original image is restored by the inverse DST. The experimental results show the effectiveness of the proposed method and its superiority over the recently proposed spatial domain DFT-based methods.
ER -
English
