Structure Tensor Based Image Interpolation Method
This work addresses image interpolation for applications requiring noise robustness and efficiency, but it is incremental as it builds on existing edge-directed methods.
The paper tackles feature-preserving image interpolation by proposing a new edge-directed super-resolution algorithm based on structure tensors, which classifies pixels into uniform regions, corners, and edges using an isotropic Gaussian filter for noise robustness and achieves higher quality and speed compared to previous methods.
Feature preserving image interpolation is an active area in image processing field. In this paper a new direct edge directed image super-resolution algorithm based on structure tensors is proposed. Using an isotropic Gaussian filter, the structure tensor at each pixel of the input image is computed and the pixels are classified to three distinct classes; uniform region, corners and edges, according to the eigenvalues of the structure tensor. Due to application of the isotropic Gaussian filter, the classification is robust to noise presented in image. Based on the tangent eigenvector of the structure tensor, the edge direction is determined and used for interpolation along the edges. In comparison to some previous edge directed image interpolation methods, the proposed method achieves higher quality in both subjective and objective aspects. Also the proposed method outperforms previous methods in case of noisy and JPEG compressed images. Furthermore, without the need for optimization in the process, the algorithm can achieve higher speed.