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.
|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