Là một dự án mã nguồn mở, jbhuang0604/SelfExSR đã đạt 641 sao trên GitHub, ngôn ngữ Matlab. Single Image Super-Resolution from Transformed Self-Exemplars (CVPR 2015)
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Single Image Super-Resolution from Transformed Self-Exemplars (CVPR 2015)
Introduction
This is the research code for the paper:
Jia-Bin Huang, Abhishek Singh, and [Narendra Ahuja] (http://vision.ai.illinois.edu/ahuja.html), "Single Image Super-Resolution from Transformed Self-Exemplars", CVPR 2015 PDF
The proposed algorithm achieves the state-of-the-art performance on image super-resolution without the need of any external training dataset, feature extraction and complicated learning algorithms. For more details, please visit our Project page.
All the datasets (Set5, Set14, Urban 100, BSD 100, Sun-Hays 80), precomputed results and visual comparisons can be found in the following sections.
Citation
If you find the code and dataset useful in your research, please consider citing:
@inproceedings{Huang-CVPR-2015,
title={Single Image Super-Resolution From Transformed Self-Exemplars},
Author = {Huang, Jia-Bin and Singh, Abhishek and Ahuja, Narendra},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={5197--5206},
Year = {2015}
}
Contents
Folder
description
cache
cached data for vanishing point detection
data
Testing images of five datasets (Set5, Set14, Urban 100, BSD 100, Sun-Hays 80). All the images have been cropped according to the desired super-resolution factor. This avoids misalignment of the groundtruth high-resolution images and the super-resolved images
external
We use the vgg_interp2 from imrender to perform bilinear interpolation
quant_eval
Quantitative evaluation code
reference
A copy of the CVPR paper and the bibtex
source
MATLAB source code
To run the algorithm on all datasets, simply run the sr_demo_bacth.m. Note that it is an educational code that is not optimized for speed. If timing is a concern, you can achieve visually similar results with small numbers of iterations, e.g., set the number of iterations opt.numIter = 5; in the file sr_init_opt.m. An example of the speed and quality trade-off can be found in Fig. 10 in the paper.
Feedbacks and comments are welcome! Feel free to contact me via [email protected].
Enjoy!
Note: For vanishing point detection only windows executable is provided (from Image Completion using Planar Structure Guidance), a cross-platform version will be included later.
Comparison with the state-of-the-art
Datasets
The full super-resolution results on Set 5, Set 14, Urban 100, BSD 100 and Sun-Hays 80 are available.
| Dataset | Image source | Download full results |
|---- | ---|----| ----|
| Set 5 | Bevilacqua et al. BMVC 2012 | link (16.1 MB)|
| Set 14 | Zeyde et al. LNCS 2010 | link (86.0 MB)|
| Urban 100 | Huang et al. CVPR 2015 | link (1.14 GB)|
| BSD 100 | Martin et al. ICCV 2001 | link (568 MB)|
| Sun-Hays 80 | Sun and Hays ICCP 2012 | link (311 MB)|
In each dataset, we include results of the state-of-the-art single image super-resolution algorithms:
Image
Description
HR
High-resolution images. All images were cropped so that each dimension is a multiplication of the super-resolution factor. This avoids the misalignment problem in the quantitative comparison.
LR
Low-resolution test images generated with bicubic kernel downsampling.
bicubic
Bicubic interpolation
nearest
Nearest-neighbor interpolation
SelfExSR
Our result
A+
R. Timofte, V. De Smet, and L. Van Gool, A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution. In Asian Conference on Computer Vision (ACCV 2014). Code available here
Abhishek
A. Singh and N. Ahuja, Super-Resolution Using Sub-Band Self-Similarity. In Asian Conference on Computer Vision (ACCV 2014). No publicly implementation available. Results were provided by the authors.
Kim
K. I. Kim and Y. Kwon, “Single-image super-resolution using sparse regression and natural image prior”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133, 2010. Code available here
Glasner
Daniel Glasner, Shai Bagon, Michal Irani. Super-Resolution From a Single Image, In International Conference on Computer Vision (ICCV 2009). No public implementation available. Results were generated by our own implementation.
ScSR
Jianchao Yang, John Wright, Thomas Huang, and Yi Ma. Image super-resolution via sparse representation. IEEE Transactions on Image Processing, Vol 19, Issue 11, pp2861-2873, 2010. Code available here
SRCNN
Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. Learning a Deep Convolutional Network for Image Super-Resolution, in European Conference on Computer Vision (ECCV 2014). Code available here
Qualitative comparison
In our supplementary material, we includde 120 sample comparisons with the state-of-the-art algorithms. Download the document here.
You can browse and compare our results with other methods via the following links.
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jbhuang0604/SelfExSR là gì?
jbhuang0604/SelfExSR (jbhuang0604/SelfExSR) là dự án Matlab trên GitHub. Theo mô tả gốc: Single Image Super-Resolution from Transformed Self-Exemplars (CVPR 2015)
Đọc đầy đủ README ở tab phía trên.
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