Super-resolution from a single image

Methods for super-resolution can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misalignments), and (ii) Example-Based super-resolution (learning correspondence between low and high resolution image patches from a database). In this paper we propose a unified framework for combining these two families of methods. We further show how this combined approach can be applied to obtain super resolution from as little as a single image (with no database or prior examples). Our approach is based on the observation that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales. Recurrence of patches within the same image scale (at subpixel misalignments) gives rise to the classical super-resolution, whereas recurrence of patches across different scales of the same image gives rise to example-based super-resolution. Our approach attempts to recover at each pixel its best possible resolution increase based on its patch redundancy within and across scales.

1. Introduction

The goal of Super-Resolution (SR) methods is to recover a high resolution image from one or more low resolution input images. Methods for SR can be broadly classified into two families of methods: (i) The classical multi-image super-resolution, and (ii) Example-Based super-resolution. In the classical multi-image SR (e.g., [12], [5], [8] to name just a few) a set of low-resolution images of the same scene are taken (at subpixel misalignments). Each low resolution image imposes a set of linear constraints on the unknown high-resolution intensity values. If enough low-resolution images are available (at subpixel shifts), then the set of equations becomes determined and can be solved to recover the high-resolution image. Practically, however, this approach is numerically limited only to small increases in resolution [3], [14] (by factors smaller than 2).