Simple Python implementation of paper:
K.He, J.Sun, and X.Tang. Guided Image Filtering. TPAMI'12.
Guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications, including edge-aware smoothing, detail enhancement, HDR compression, image matting/feathering, dehazing, joint upsampling, etc.
Reimplementation of Figure 2 in paper and visualization on gray image.
And visualization on color image for denoising.
This repository depends on Python modules listed below.
- numpy
- matplotlib
- opencv
If you want to run demo main.py by yourself, you have to install those modules in advance.
On the other hand, core of guided filter only depends on numpy, and you can check it for more.
from core.filter import GuidedFilter
# load your image
GF = GuidedFilter(image, radius, eps)
GF.filter(filtering_input)You can run demo for quick visualization on guided filter by
> python main.pyThe MIT License 2017 (c) Lisabug