Reconstruction of hidden images using wavelet transform and an entropy-maximization algorithm
Reconstruction of hidden images using wavelet transform and an entropy-maximization algorithm
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发表时间：
2006-12
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通讯作者：
Naoto Nakamura;S. Takano;Y. Okada;K. Niijima
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作者：
Naoto Nakamura;S. Takano;Y. Okada;K. Niijima
提出了一种基于小波变换和最大熵算法的图像盲分离方法。我们的盲分离算法是Bell-Sejnowsky和Amari提出的熵最大化算法的改进版本。这些算法对于具有超高斯分布的信号（诸如语音和音频）工作良好。该方法是将改进的算法应用于自然图像的小波系数，其分布接近于超高斯。我们的方法成功地重建了隐藏在另外12幅图像中的12幅图像，这些图像彼此相似。
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.