A parallel implementation of ant colony optimization
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Image Thresholding Using Ant Colony Optimization
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Color reduction based on ant colony
Pattern Recognition Letters
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Ant Colony Optimization for Image Regularization Based on a Nonstationary Markov Modeling
IEEE Transactions on Image Processing
A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding
IEEE Transactions on Image Processing
Image edge detection using variation-adaptive ant colony optimization
Transactions on computational collective intelligence V
Circle detection using electro-magnetism optimization
Information Sciences: an International Journal
An evolutionary image matching approach
Applied Soft Computing
Depth image enlargement using an evolutionary approach
Image Communication
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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.