Reconstruction of hidden images using wavelet transform and an entropy-maximization algorithm

Naoto Nakamura, Shigeru Takano, Yoshihiro Okada, Koichi Niijima

Research output: Contribution to conferencePaper

Abstract

This paper proposes a blind image separation method using wavelet transform and an entropy-maximization algorithm. Our blind separation algorithm is an improved version of the entropy-maximization algorithms presented by Bell- Sejnowsky and Amari. These algorithms work well for signals having a supergaussian distribution, such as speech and audio. The proposed method is to apply the improved algorithm to the wavelet coefficients of a natural image, whose distribution is close to supergaussian. Our method successfully reconstruct twelve images hidden in another twelve images which are similar each other.

Original languageEnglish
Publication statusPublished - 2006
EventThe 14th European Signal Processing Conference -
Duration: Sep 4 2006Sep 8 2006

Conference

ConferenceThe 14th European Signal Processing Conference
Abbreviated titleEUSIPCO 2006
Period9/4/069/8/06

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All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Nakamura, N., Takano, S., Okada, Y., & Niijima, K. (2006). Reconstruction of hidden images using wavelet transform and an entropy-maximization algorithm. Paper presented at The 14th European Signal Processing Conference, .