/mixed-norm-power-constrained-sparse-representation

Power-Constrained Image Contrast Enhancement Through Sparse Representation by Joint Mixed-Norm Regularization

Primary LanguagePythonApache License 2.0Apache-2.0

GPU Accelerated PCSR implementation with Python

Power-Constrained Image Contrast Enhancement Through Sparse Representation by Joint Mixed-Norm Regularization

Jia-Li Yin, Bo-Hao Chen, En-Hung Lai, and Ling-Feng Shi

Prerequisites:

  • Linux
  • Anaconda
  • CUDA 9.2
  • cuDNN 7.2.1
  • Numbapro 0.23.1
  • Python 2.7
  • Numpy 1.10.4
  • pip 19.1.1
  • OpenCV 3.4.1

It was tested and runs under the following OSs:

  • Ubuntu 18.04
  • Ubuntu 16.04

Might work under others, but didn't get to test any other OSs just yet.

Getting Started:

Installation

  • create a PCSR environment
conda create -n PCSR numbapro
  • activate the PCSR environment
source activate PCSR
  • install opencv
conda install opencv
  • install python-spams
conda install -c conda-forge python-spams

Testing

  • To test the PCSR model:
python main.py

The test results will be saved in: ./Results/.

Citation:

@ARTICLE{yin2019PCCE, 
author={J. {Yin} and B. {Chen} and E. {Lai} and L. {Shi}}, 
journal={IEEE Transactions on Circuits and Systems for Video Technology},  
title={Power-Constrained Image Contrast Enhancement Through Sparse Representation by Joint Mixed-Norm Regularization},  
year={2020},  
volume={30}, 
number={8},  
pages={2477-2488},}