Abhishek Gola
Added NAFNet quantized model for deblurring DNN sample (#295)
cca075c

NAFNet

NAFNet is a lightweight image deblurring model that eliminates nonlinear activations to achieve state-of-the-art performance with minimal computational cost.

Notes:

  • Model source: .pth.
  • ONNX Model link: ONNX

Requirements

Install latest OpenCV >=5.0.0 and CMake >= 3.22.2 to get started with.

Demo

Python

Run the following command to try the demo:

# deblur the default input image
python demo.py
# deblur the user input image
python demo.py --input /path/to/image

# get help regarding various parameters
python demo.py --help

C++

# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build

# deblur the default input image
./build/demo
# deblur the user input image
./build/demo --input=/path/to/image
# get help messages
./build/demo -h

Example outputs

licenseplate_motion

License

All files in this directory are licensed under MIT License.

Reference