Ant colony optimization for wavelet-based image interpolation using a three-component exponential mixture model

Wavelet-based image interpolation typically treats the input image as the low frequency subbands of an unknown wavelet-transformed high-resolution image, and then produces the unknown high-resolution image by estimating the wavelet coefficients of the high frequency subbands. For that, a new approach is proposed in this paper, the contribution of which are twofold. First, unlike that the conventional Gaussian mixture (GM) model only exploits the magnitude information of the wavelet coefficients, a three-component exponential mixture (TCEM) model is proposed in this paper to investigate both the magnitude information and the sign information of the wavelet coefficients. The proposed TCEM model consists of a Gaussian component, a positive exponential component and a negative exponential component. Second, to address the parameter estimation challenge of the proposed TCEM model, the ant colony optimization (ACO) technique is exploited in this paper to classify the wavelet coefficients into one of three components of the proposed TCEM model for estimating their parameters. Experiments are conducted to demonstrate that the proposed approach outperform a number of approaches developed in the literature.