Gradient Based Edge Preserving Interpolation and Its Application to Super-Resolution

Abstract

Super-resolution is a process for obtaining high quality and high resolution images from a set of or only one low-resolution image. The most practical one for image super-resolution is reconstruction-based method, which minimizes the difference between observed low-resolution images and the estimation for high resolution image. Therein, interpolation step plays a key role for the estimated high resolution image quality. Usually, the conventional bilinear or bicubic methods are used in the reconstruction-based super-resolution. However, these conventional interpolations generally lead to blurring on edge regions and need more time for convergence in reconstruction-based super-resolution method. Therefore, this paper propose a gradient based edge preserving interpolation method, which can reduce not only artifact noise but also blurring near edge regions in the estimated high resolution image. Furthermore, our proposed interpolation method can also solve large complexity and time-consuming problem in the recently developed New Edge-directed interpolation, which usually can achieve sharp edge in the high resolution reconstructed image. Experiments validate that our proposed interpolation method for image super-resolution is more effective than the conventional interpolation ones.