Adaptive Image Restoration Using Constrained Deconvolution

Hiroshi KONDO; Tadashi NAGATA

Adaptive Image Restoration Using Constrained Deconvolution

Hiroshi KONDO, Tadashi NAGATA

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An adaptive image restoration technique is presented. This is an extended version of the spatially invariant image restoration technique using constrained deconvolution. The aim of the extension is to improve the characteristics of the original filter. The proposed filter partitions the image into three types of regions. For each region this filter has a different impulse response. The most distinctive feature of this filter is that the form of the impulse response varies according to the direction of the edges in the image. It becomes an ordinary inverse filter irrespective of each region, however, when additive noise does not exist in the degraded image. Simulation results show that the observation noise is suppressed, while the edges are preserved.

Publication: IEICE TRANSACTIONS on transactions Vol.E72-E No.11 pp.1243-1250

Publication Date: 1989/11/25

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Type of Manuscript: PAPER

Category: Image Processing, Computer Graphics and Pattern Recognition

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Hiroshi KONDO
Tadashi NAGATA

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Hiroshi KONDO, Tadashi NAGATA, "Adaptive Image Restoration Using Constrained Deconvolution" in IEICE TRANSACTIONS on transactions, vol. E72-E, no. 11, pp. 1243-1250, November 1989, doi: .
Abstract: An adaptive image restoration technique is presented. This is an extended version of the spatially invariant image restoration technique using constrained deconvolution. The aim of the extension is to improve the characteristics of the original filter. The proposed filter partitions the image into three types of regions. For each region this filter has a different impulse response. The most distinctive feature of this filter is that the form of the impulse response varies according to the direction of the edges in the image. It becomes an ordinary inverse filter irrespective of each region, however, when additive noise does not exist in the degraded image. Simulation results show that the observation noise is suppressed, while the edges are preserved.
URL: https://global.ieice.org/en_transactions/transactions/10.1587/e72-e_11_1243/_p

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@ARTICLE{e72-e_11_1243,
author={Hiroshi KONDO, Tadashi NAGATA, },
journal={IEICE TRANSACTIONS on transactions},
title={Adaptive Image Restoration Using Constrained Deconvolution},
year={1989},
volume={E72-E},
number={11},
pages={1243-1250},
abstract={An adaptive image restoration technique is presented. This is an extended version of the spatially invariant image restoration technique using constrained deconvolution. The aim of the extension is to improve the characteristics of the original filter. The proposed filter partitions the image into three types of regions. For each region this filter has a different impulse response. The most distinctive feature of this filter is that the form of the impulse response varies according to the direction of the edges in the image. It becomes an ordinary inverse filter irrespective of each region, however, when additive noise does not exist in the degraded image. Simulation results show that the observation noise is suppressed, while the edges are preserved.},
keywords={},
doi={},
ISSN={},
month={November},}

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TY - JOUR
TI - Adaptive Image Restoration Using Constrained Deconvolution
T2 - IEICE TRANSACTIONS on transactions
SP - 1243
EP - 1250
AU - Hiroshi KONDO
AU - Tadashi NAGATA
PY - 1989
DO -
JO - IEICE TRANSACTIONS on transactions
SN -
VL - E72-E
IS - 11
JA - IEICE TRANSACTIONS on transactions
Y1 - November 1989
AB - An adaptive image restoration technique is presented. This is an extended version of the spatially invariant image restoration technique using constrained deconvolution. The aim of the extension is to improve the characteristics of the original filter. The proposed filter partitions the image into three types of regions. For each region this filter has a different impulse response. The most distinctive feature of this filter is that the form of the impulse response varies according to the direction of the edges in the image. It becomes an ordinary inverse filter irrespective of each region, however, when additive noise does not exist in the degraded image. Simulation results show that the observation noise is suppressed, while the edges are preserved.
ER -