Giter Site home page Giter Site logo

guohong365 / dncnn-tensorflow Goto Github PK

View Code? Open in Web Editor NEW

This project forked from wbhu/dncnn-tensorflow

0.0 0.0 0.0 117.21 MB

:octocat::octocat:A tensorflow implement of the paper "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising"

License: GNU General Public License v3.0

Python 100.00%

dncnn-tensorflow's Introduction

DnCNN-tensorflow

AUR Contributions welcome

A tensorflow implement of the TIP2017 paper Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Model Architecture

graph

Results

compare

  • BSD68 Average Result

The average PSNR(dB) results of different methods on the BSD68 dataset.

Noise Level BM3D WNNM EPLL MLP CSF TNRD DnCNN-S DnCNN-B DnCNN-tensorflow
25 28.57 28.83 28.68 28.96 28.74 28.92 29.23 29.16 29.17
  • Set12 Average Result
Noise Level DnCNN-S DnCNN-tensorflow
25 30.44 30.38

Requirements

tensorflow >= 1.4
numpy
opencv

Dataset

I used the BDS500 dataset for training, you can download it here: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_bsds500.tgz It contains 500 RGB images, 400 for training and 100 for testing.

Data preprocessing and noise generation

Before training, you have to rescale the images to 180x180 and adding noise to them. The folder structure is supposed to be:

./data/train/original  for the 180x180 original train images
./data/train/noisy  for the 180x180 noisy train images
./data/test/original  for the 180x180 original test images
./data/test/noisy  for the 180x180 noisy test images

You need the original files for testing just to calculate the PSNR. You can denoise without original files: just put the noisy files also in ./data/test/original .

Train

$ python main.py
(note: You can add command line arguments according to the source code, for example
    $ python main.py --batch_size 64 )

Test

$ python main.py --phase test

dncnn-tensorflow's People

Contributors

clausmichele avatar lizhiyuanustc avatar sdlpkxd avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.