An adaptive covariance-based edge diffusion image enlargement method

We discuss image or video enlargement methods aimed at computationally constrained environments. Traditional enlargement algorithms such as linear or cubic interpolation have been applied in many applications. However the performance of these approaches is limited by artifacts such as blurring and jagged edges. More sophisticated iterative and learning-based algorithms have been proposed to address these issues, but they typically involve very high computational complexity. We present an adaptive covariance-based edge diffusion (ACED) image enlargement method that offers both good performance and low complexity. Different from other edge-directed interpolation algorithms, the proposed method uses combination of a novel edge-directed judgment which can choose different spread templates adaptively to estimate local covariance coefficients and edge diffusion to reduce artifacts. Experimental results show that the proposed method gives performs well both in terms of subjective quality as well as objective measures.