IEICE TRANSACTIONS on Information
Subspace Information Criterion for Image Restoration--Optimizing Parameters in Linear Filters
Summary :
Most of the image restoration filters proposed so far include parameters that control the restoration properties. For bringing out the optimal restoration performance, these parameters should be determined so as to minimize a certain error measure such as the mean squared error (MSE) between the restored image and original image. However, this is not generally possible since the unknown original image itself is required for evaluating MSE. In this paper, we derive an estimator of MSE called the subspace information criterion (SIC), and propose determining the parameter values so that SIC is minimized. For any linear filter, SIC gives an unbiased estimate of the expected MSE over the noise. Therefore, the proposed method is valid for any linear filter. Computer simulations with the moving-average filter demonstrate that SIC gives a very accurate estimate of MSE in various situations, and the proposed procedure actually gives the optimal parameter values that minimize MSE.
- Publication
- IEICE TRANSACTIONS on Information Vol.E84-D No.9 pp.1249-1256
- Publication Date
- 2001/09/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Image Processing, Image Pattern Recognition
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Masashi SUGIYAMA, Daisuke IMAIZUMI, Hidemitsu OGAWA, "Subspace Information Criterion for Image Restoration--Optimizing Parameters in Linear Filters" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 9, pp. 1249-1256, September 2001, doi: .
Abstract: Most of the image restoration filters proposed so far include parameters that control the restoration properties. For bringing out the optimal restoration performance, these parameters should be determined so as to minimize a certain error measure such as the mean squared error (MSE) between the restored image and original image. However, this is not generally possible since the unknown original image itself is required for evaluating MSE. In this paper, we derive an estimator of MSE called the subspace information criterion (SIC), and propose determining the parameter values so that SIC is minimized. For any linear filter, SIC gives an unbiased estimate of the expected MSE over the noise. Therefore, the proposed method is valid for any linear filter. Computer simulations with the moving-average filter demonstrate that SIC gives a very accurate estimate of MSE in various situations, and the proposed procedure actually gives the optimal parameter values that minimize MSE.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_9_1249/_p
Copy
@ARTICLE{e84-d_9_1249,
author={Masashi SUGIYAMA, Daisuke IMAIZUMI, Hidemitsu OGAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Subspace Information Criterion for Image Restoration--Optimizing Parameters in Linear Filters},
year={2001},
volume={E84-D},
number={9},
pages={1249-1256},
abstract={Most of the image restoration filters proposed so far include parameters that control the restoration properties. For bringing out the optimal restoration performance, these parameters should be determined so as to minimize a certain error measure such as the mean squared error (MSE) between the restored image and original image. However, this is not generally possible since the unknown original image itself is required for evaluating MSE. In this paper, we derive an estimator of MSE called the subspace information criterion (SIC), and propose determining the parameter values so that SIC is minimized. For any linear filter, SIC gives an unbiased estimate of the expected MSE over the noise. Therefore, the proposed method is valid for any linear filter. Computer simulations with the moving-average filter demonstrate that SIC gives a very accurate estimate of MSE in various situations, and the proposed procedure actually gives the optimal parameter values that minimize MSE.},
keywords={},
doi={},
ISSN={},
month={September},}
Copy
TY - JOUR
TI - Subspace Information Criterion for Image Restoration--Optimizing Parameters in Linear Filters
T2 - IEICE TRANSACTIONS on Information
SP - 1249
EP - 1256
AU - Masashi SUGIYAMA
AU - Daisuke IMAIZUMI
AU - Hidemitsu OGAWA
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2001
AB - Most of the image restoration filters proposed so far include parameters that control the restoration properties. For bringing out the optimal restoration performance, these parameters should be determined so as to minimize a certain error measure such as the mean squared error (MSE) between the restored image and original image. However, this is not generally possible since the unknown original image itself is required for evaluating MSE. In this paper, we derive an estimator of MSE called the subspace information criterion (SIC), and propose determining the parameter values so that SIC is minimized. For any linear filter, SIC gives an unbiased estimate of the expected MSE over the noise. Therefore, the proposed method is valid for any linear filter. Computer simulations with the moving-average filter demonstrate that SIC gives a very accurate estimate of MSE in various situations, and the proposed procedure actually gives the optimal parameter values that minimize MSE.
ER -
English
