State Sharing Methods in Statistical Fluctuation for Image Restoration

Michiharu MAEDA; Hiromi MIYAJIMA

State Sharing Methods in Statistical Fluctuation for Image Restoration

Michiharu MAEDA, Hiromi MIYAJIMA

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This paper presents novel algorithms for image restoration by state sharing methods with the stochastic model. For inferring the original image, in the first approach, a degraded image with gray scale transforms into binary images. Each binary image is independently inferred according to the statistical fluctuation of stochastic model. The inferred images are returned to a gray-scale image. Furthermore the restored image is constructed from the average of the plural inferred images. In the second approach, the binary state is extended to a multi-state, that is, the degraded image with Q state is transformed into n images with τ state and image restoration is performed. The restoration procedure is described as follows. The degraded image with Q state is prepared and is transformed into n images with τ state. The n images with τ state are independently inferred by the stochastic model and are returned to one image. Moreover the restored image is constructed from the average of the plural inferred images. Finally, the properties of the present approaches are described and the validity of them is confirmed through numerical experiments.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E87-A No.9 pp.2347-2354

Publication Date: 2004/09/01

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Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications)

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Michiharu MAEDA, Hiromi MIYAJIMA, "State Sharing Methods in Statistical Fluctuation for Image Restoration" in IEICE TRANSACTIONS on Fundamentals, vol. E87-A, no. 9, pp. 2347-2354, September 2004, doi: .
Abstract: This paper presents novel algorithms for image restoration by state sharing methods with the stochastic model. For inferring the original image, in the first approach, a degraded image with gray scale transforms into binary images. Each binary image is independently inferred according to the statistical fluctuation of stochastic model. The inferred images are returned to a gray-scale image. Furthermore the restored image is constructed from the average of the plural inferred images. In the second approach, the binary state is extended to a multi-state, that is, the degraded image with Q state is transformed into n images with τ state and image restoration is performed. The restoration procedure is described as follows. The degraded image with Q state is prepared and is transformed into n images with τ state. The n images with τ state are independently inferred by the stochastic model and are returned to one image. Moreover the restored image is constructed from the average of the plural inferred images. Finally, the properties of the present approaches are described and the validity of them is confirmed through numerical experiments.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/e87-a_9_2347/_p

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@ARTICLE{e87-a_9_2347,
author={Michiharu MAEDA, Hiromi MIYAJIMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={State Sharing Methods in Statistical Fluctuation for Image Restoration},
year={2004},
volume={E87-A},
number={9},
pages={2347-2354},
abstract={This paper presents novel algorithms for image restoration by state sharing methods with the stochastic model. For inferring the original image, in the first approach, a degraded image with gray scale transforms into binary images. Each binary image is independently inferred according to the statistical fluctuation of stochastic model. The inferred images are returned to a gray-scale image. Furthermore the restored image is constructed from the average of the plural inferred images. In the second approach, the binary state is extended to a multi-state, that is, the degraded image with Q state is transformed into n images with τ state and image restoration is performed. The restoration procedure is described as follows. The degraded image with Q state is prepared and is transformed into n images with τ state. The n images with τ state are independently inferred by the stochastic model and are returned to one image. Moreover the restored image is constructed from the average of the plural inferred images. Finally, the properties of the present approaches are described and the validity of them is confirmed through numerical experiments.},
keywords={},
doi={},
ISSN={},
month={September},}

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TY - JOUR
TI - State Sharing Methods in Statistical Fluctuation for Image Restoration
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2347
EP - 2354
AU - Michiharu MAEDA
AU - Hiromi MIYAJIMA
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E87-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2004
AB - This paper presents novel algorithms for image restoration by state sharing methods with the stochastic model. For inferring the original image, in the first approach, a degraded image with gray scale transforms into binary images. Each binary image is independently inferred according to the statistical fluctuation of stochastic model. The inferred images are returned to a gray-scale image. Furthermore the restored image is constructed from the average of the plural inferred images. In the second approach, the binary state is extended to a multi-state, that is, the degraded image with Q state is transformed into n images with τ state and image restoration is performed. The restoration procedure is described as follows. The degraded image with Q state is prepared and is transformed into n images with τ state. The n images with τ state are independently inferred by the stochastic model and are returned to one image. Moreover the restored image is constructed from the average of the plural inferred images. Finally, the properties of the present approaches are described and the validity of them is confirmed through numerical experiments.
ER -