# guided-filter

**Repository Path**: zhencinigedaxiguaer/guided-filter

## Basic Information

- **Project Name**: guided-filter
- **Description**: Implementation of Guided Image Filtering.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2021-01-04
- **Last Updated**: 2023-04-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Guided Filter
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.

## Visualization of results

Reimplementation of Figure 2 in paper and visualization on gray image.
![Guided Filter on gray image](data/cat_result.png)

And visualization on color image for denoising.
![Guided Filter on color image](data/lenna_result.png)

## Dependencies

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.

## Usage

```python
from core.filter import GuidedFilter
# load your image
GF = GuidedFilter(image, radius, eps)
GF.filter(filtering_input)
```

### Demo
You can run demo for quick visualization on guided filter by
```bash
> python main.py
```

## License

The MIT License 2017 (c) Lisabug