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https://github.com/xinario/defocus_segmentation
LBP-based segmentation of defocus blur
https://github.com/xinario/defocus_segmentation
blur-detection computer-vision defocus-blur defocus-estimation defocus-map defocus-map-estimation defocus-segmentation lbp local-binary-patterns low-depth-of-field markov-random-field out-of-focus segmentation sharpness
Last synced: 5 days ago
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LBP-based segmentation of defocus blur
- Host: GitHub
- URL: https://github.com/xinario/defocus_segmentation
- Owner: xinario
- Created: 2016-10-05T19:07:09.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-08-18T17:43:41.000Z (over 6 years ago)
- Last Synced: 2024-11-10T19:18:01.528Z (2 months ago)
- Topics: blur-detection, computer-vision, defocus-blur, defocus-estimation, defocus-map, defocus-map-estimation, defocus-segmentation, lbp, local-binary-patterns, low-depth-of-field, markov-random-field, out-of-focus, segmentation, sharpness
- Language: Matlab
- Homepage:
- Size: 2.12 MB
- Stars: 64
- Watchers: 5
- Forks: 11
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# LBP-Based Segmentation of Defocus Blur
## Update2:
Add a python implementation to compute the sharpness metric.
## Update1:
The blur maps for the 1000 images in the [blur segmentation dataset](http://www.cse.cuhk.edu.hk/%7Eleojia/projects/dblurdetect/dataset.html) produced by our algorithm can be found [here](https://1drv.ms/u/s!Aj4IQl4ug0_9hEANDaY7WjuOQkxW) for easy comparison.
##
This repo provides the code to reproduce our defocus segmentaion results in our [paper](https://ieeexplore.ieee.org/abstract/document/7403985/).
## How to use
### Prerequistites
- Matlab 2016a
If you want to try out the python script, you need
- Python 3
- Opencv 3 (pip install opencv-python)
### Getting Started
- Clone this repo:
```bash
git clone [email protected]:xinario/defocus_segmentation.git
```
- In Matlab, change your project directiory to /defocus_segmentation then run demo.m
- Note that matlab implementation was used by default to compute the proposed sharpness metric. But you can also switch to .mex code to gain some speed boost. The sharpness metric implementation in .mex code was based on integral image and can run in real time on a single core cpu.
```
Compute the LBP-based sharpness measure using the mex version.
1. Install [mexopencv](https://github.com/kyamagu/mexopencv)
2. Copy lbpSharpness.cpp to /src/+cv
3. Run mexopencv.make() in Matlab command line to compile the provided function.
4. Comment out line 14 and uncomment line 18, 19 in localSharpScoreLBP.m, then you are good to go.
All the results reported in the paper were produced by the mex verison of LBP-based sharpness.
```
- To use python script
```
python lbpSharpness.py --input ./images/out_of_focus0080.JPG
```
### Citations
If you find it useful and are using the code/model/dataset provided here in a publication, please cite our paper:
Yi, Xin, and Mark Eramian. "LBP-based segmentation of defocus blur." IEEE transactions on image processing 25.4 (2016): 1626-1638.
### Acknowlegements
The alpha matting code comes from Levin, 2006:
>A. Levin D. Lischinski and Y. Weiss. *A Closed Form Solution to Natural Image Matting*.
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2006, New York.
The multi-scale inference code was adopted form Jianping Shi, 2014:
>Jianping Shi, Li Xu, Jiaya Jia. *Discriminative Blur Detection Features*.
IEEE Conference on Computer Vision and Pattern Recognition, 2014.