Principal Components Analysis-Based Edge-Directed Image Interpolation

Abstract

This paper presents an edge-directed, noniterative image interpolation algorithm. In the proposed algorithm, the gradient directions are explicitly estimated with a statistical-based approach. The local dominant gradient directions are obtained by using principal components analysis (PCA) on the four nearest gradients. The angles of the whole gradient plane are divided into four parts, and each gradient direction falls into one part. Then we implement the interpolation with one-dimention (1-D) cubic convolution interpolation perpendicular to the gradient direction. Compared to the state of-the-art interpolation methods, simulation results show that the proposed PCA-based edge-directed interpolation method preserves edges well while maintaining a high PSNR value.