AntShrink: Ant colony optimization for image shrinkage

Wavelet shrinkage is an image denoising technique based on the concept of thresholding the wavelet coefficients. The key challenge of wavelet shrinkage is to find an appropriate threshold value, which is typically controlled by the signal variance. To tackle this challenge, a new image shrinkage approach, called AntShrink, is proposed in this paper. The proposed approach exploits the intra-scale dependency of the wavelet coefficients to estimate the signal variance only using the homogeneous local neighboring coefficients. This is in contrast to that all local neighboring coefficients are used in the conventional shrinkage approaches. Furthermore, to determine the homogeneous local neighboring coefficients, the ant colony optimization (ACO) technique is used in this paper to classify the wavelet coefficients. Experimental results are provided to show that the proposed approach outperforms several image denoising approaches developed in the literature.