Abstract—Edge-directed image super resolution (SR) focuses on ways to remove edge artifacts in upsampled images. Under large magnification, however, textured regions become blurred and appear homogenous, resulting in a super-resolution image that looks unnatural. Alternatively, learning-based SR approaches use a large database of exemplar images for “hallucinating” detail. The quality of the upsampled image, especially about edges, is dependent on the suitability of the training images. This paper aims to combine the benefits of edge-directed SR with those of learning-based SR. In particular, we propose an approach to extend edge-directed super-resolution to include detail from an image/texture example provided by the user (e.g., from the Internet). A significant benefit of our approach is that only a single exemplar image is required to supply the missing detail – strong edges are obtained in the SR image even if they are not present in the example image due to the combination of the edge-directed approach. In addition, we can achieve quality results at very large magnification, which is often problematic for both edge-directed and learning-based approaches.

Publications:

• Super Resolution using Edge Prior and Single Image Detail Synthesis
  Yu-Wing Tai, Shuaicheng Liu, Michael S. Brown and Stephen Lin, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010

Source Codes (Partial) in C++

Related Project:
Colorization for Single Image Super Resolution
Perceptually-Inspired and Edge-Directed Color Image Super-Resolution

BibTex:

@inproceedings{Tai08cvpr,
author = {Yu-Wing Tai and Shuaicheng Liu and Michael S. Brown and Stephen Lin},
title = {Super Resolution using Edge Prior and Singale Image Detail Synthesis},
booktitle = {CVPR},
year = {2010}
}

---

Results:

Nearest Neighbor LR-RMS 0.60 HR-RMS 11.87	Bicubic LR-RMS 0.61 HR-RMS 9.06	Back Projection LR-RMS 3.05 HR-RMS 10.66
Gradient Profile Prior [CVPR'08] LR-RMS 1.89 HR-RMS 7.64	Learning [IJCV'00] LR-RMS 3.14 HR-RMS 16.59	Our result with sand texture LR-RMS 3.45 HR-RMS 15.89
Our result with zebra texture LR-RMS 3.10 HR-RMS 14.85	Our result with circle image LR-RMS 2.17 HR-RMS 7.32	Ground Truth LR-RMS 0.00 HR-RMS 0.00
10x super-resolution on a synthetic example. Our approach generates different results depending on the supplied texture. The lower left corner shows the result image after 10x downsampling. Note that for all results, the down-sampled images are approximately identical. Listed below each result are the LR-RMS errors (RMS errors with respect to the low resolution input), and the HR-RMS errors (RMS errors with respect to the high resolution ground truth image).

 

Input and example image	Learning [IJCV'00] HR-RMS 24.3 MSSIM 0.62	Alpha Channel [CVPR'07] HR-RMS 9.3 MSSIM 0.70
Gradient Profile Prior [CVPR'08] HR-RMS 8.4 MSSIM 0.75	Our Result HR-RMS 10.6 MSSIM 0.77	Ground Truth
Our 10x magnification
Face with freckles. (a-e) 4x magnification result of various approaches. (f) Ground truth. (g) Our result with a 10x magnification. The HR-RMS errors and the MSSIM score with respect to the 4x ground truth image are listed below each result.

 

Input and example image	Gradient Profile [CVPR'08]	Learning [IJCV'00]	Our Results