Parallel Implementation of Edge-Directed Image Interpolation on a Graphics Processing Unit

The edge-directed interpolation scheme is a non-iterative, orientation-adaptive method to enhance image resolution with better visual effect than conventional interpolation methods. It interpolates the missing pixels based on the covariance of a high-resolution image estimated from the covariance of the low-resolution image. In spite of the impressive performance, the computational complexity of covariance-based adaptation is significantly higher than that of the conventional linear interpolation algorithms. In this paper, we propose a GPU-based massively parallel version of the edge-directed interpolation scheme. A speedup of 61.7x can be achieved with respect to its single-threaded CPU counterpart in the host computer.