抄録
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.
| 元の言語 | 英語 |
|---|---|
| 出版物ステータス | 出版済み - 2006 |
| イベント | The 14th European Signal Processing Conference - 継続期間: 9 4 2006 → 9 8 2006 |
会議
| 会議 | The 14th European Signal Processing Conference |
|---|---|
| 略称タイトル | EUSIPCO 2006 |
| 期間 | 9/4/06 → 9/8/06 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering
これを引用
Reconstruction of hidden images using wavelet transform and an entropy-maximization algorithm. / Nakamura, Naoto; Takano, Shigeru; Okada, Yoshihiro; Niijima, Koichi.
2006. 論文発表場所 The 14th European Signal Processing Conference, .研究成果: 会議への寄与タイプ › 論文
}
TY - CONF
T1 - Reconstruction of hidden images using wavelet transform and an entropy-maximization algorithm
AU - Nakamura, Naoto
AU - Takano, Shigeru
AU - Okada, Yoshihiro
AU - Niijima, Koichi
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84862621071&partnerID=8YFLogxK
M3 - Paper
ER -