This is a reference MATLAB implementation of the Sub-window Variance filter described in our article Multi-scale Image Decomposition Using a Local Statistical Edge Model. Our filter uses Summed Area Table (integral image) as an acceleration means, and it is also gradient-preserving, i.e. has no gradient reversal problem. (paper preprint here)
This code has been tested on MATLAB R2019b.
By using svf.m, you may quickly filter an image with the following command and have the result displayed in MATLAB.
[A, result] = svf(double(imread('cat.png'))/255.0, 3, 0.025);
imshow(result);
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| Input | Per-pixel preservation (A) | Filtered (result) |
Please see svEnhance.m for an example of how to enhance the image detail.
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| Both medium and fine details enhanced |
If you have used this code in your research or work, please consider citing our paper:
@INPROCEEDINGS{9483837,
author={Wong, Kin-Ming},
booktitle={2021 IEEE 7th International Conference on Virtual Reality (ICVR)},
title={Multi-scale Image Decomposition Using a Local Statistical Edge Model},
year={2021},
volume={},
number={},
pages={10-18},
doi={10.1109/ICVR51878.2021.9483837}
}