ガウス混成モデルによるボケ像修正ウィナーフィルタ  [in Japanese] Wiener Filter based on the Gaussian Mixture Distribution Model for Blur Restoration  [in Japanese]

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Abstract

本論文では,ガウス混成モデルに基づくウィナーフィルタ(WF)を雑音の重畳したボケ像の修正フィルタに応用する.このWF法は,画像の局所ブロック内の信号列を有限個のガウス定常過程に分類し,各過程毎にWFを適用することで,画像の局所的統計量の変化に対する適応化を図るものである.本法をボケ像修正フィルタに適用する場合,ボケによる信号電力の減少のため,モデルの検出誤差が増加し,復元能力が低下することが考えられる.その一対策法として混成モデルのパラメータ検出に正則化法を導入する。最後に,シミュレーション実験結果を示し,有効性を明らかにする.

In this paper, a Wiener Filter (WF) based on the Gaussian mixtured distribution model is adopted to restoration of blured mages with additive noise. In this method, image signals in local blocks are classified into finite number of Gaussian processes and WF's are applied to each process individually to adapt WF to local variation of image statistics. In the case of restoring bured images, detection of the model parameter is significantly suffered with noise and performance of restoration is reduced. To improve detection of the model parameter, a regularization method is adopted. Finally simulation results show the efficiency of the proposed method.

Journal

IPSJ SIG Notes   [List of Volumes]

IPSJ SIG Notes 99(107), 93-98, 1999-12-16  [Table of Contents]

Information Processing Society of Japan (IPSJ)

References:  7

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Codes

  • NII Article ID (NAID) :
    110002935996
  • NII NACSIS-CAT ID (NCID) :
    AN10438399
  • Text Lang :
    JPN
  • Article Type :
    ART
  • ISSN :
    09196072
  • NDL Article ID :
    5339137
  • NDL Source Classification :
    ZM13(科学技術--科学技術一般--データ処理・計算機)
  • NDL Call No. :
    Z14-1121
  • Databases :
    CJP  NDL  NII-ELS