A Single-Image Super-Resolution Method Based on Progressive-Iterative Approximation

In this paper, a novel single image super-resolution (SR) method based on progressive-iterative approximation is proposed. To preserve textures and clear edges, the image SR reconstruction is treated as an image progressive-iterative fitting procedure and achieved by iterative interpolation. Due to different features in different regions, we first employ the nonsubsampled contourlet transform (NSCT) to divide the image into smooth regions, texture regions, and edges. Then, a hybrid interpolation scheme based on curves and surfaces is proposed, which differs from the traditional surface interpolation methods. Specifically, smooth regions are interpolated by the non-uniform rational basis spline (NURBS) surface geometric iteration. To retain textures, control points are increased, and the progressive-iterative approximation of the NURBS surface is employed to interpolate the texture regions. By considering edges in an image as curve segments that are connected by pixels with dramatic changes, we use NURBS curve progressive-iterative approximation to interpolate the edges, which sharpens the edges and can maintain the image edge structure without jaggy and block artifacts. The experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of both subjective and objective measures.

I. Introduction

Single image super-resolution (SISR) aims to reconstruct a high-resolution (HR) image from a single low-resolution (LR) image. SISR is receiving increasing attention because of its extensive application in many fields, such as image-based medical analysis, satellite remote sensing imaging, video surveillance, and computer vision. Various SISR methods have been reported. Overall, the SISR approaches can be divided into three categories: reconstruction-based methods, interpolation-based methods, and learning-based methods.