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 language | English |
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Journal | European Signal Processing Conference |
Publication status | Published - 2006 |
Event | 14th European Signal Processing Conference, EUSIPCO 2006 - Florence, Italy Duration: Sept 4 2006 → Sept 8 2006 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering