GAN implements image denoising (Pytorch framework implementation)

Table of contents

1. Generate background

2. Data Collection

3. Overall Design Ideas

(1) Generate model 

(2) Discriminant model

4. Model denoising effect

(1) Demo demonstration effect

5. GitHub code download

6. Model download

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GAN principle and Pytorch framework to realize GAN (relatively easy to understand)

Pytorch framework implements DCGAN (relatively easy to understand)

The basic principle of CycleGAN and the implementation of Pytorch framework

The basic principle of WGAN and the implementation of WGAN by Pytorch

Pytorch framework implements WGAN-GP

1. Generate background

        Whether it is in our life or in the industrial world, when we acquire images, we all hope to get a clear image, so that we can see the objects and background in the image more clearly, and for the industrial world, this is also very important . For example, in our WeChat shooting system, sometimes due to weather or other reasons, the captured images are blurred or noisy, so the images may not be clear, which is very unfavorable for understanding geography, architecture or natural disasters. In order to remove Noise in the image, this article mainly uses the generative confrontation network (GAN) to achieve image denoising.

2. Data Collection

Tip: This article is mainly to remove Gaussian noise in the image. In fact, there are many types of noise in the image. A clear image will be collected, and the program will be used to add Gaussian noise or salt and pepper noise to the image to obtain a noisy image while retaining the original clear image.

Dataset download:

Link: https://pan.baidu.com/s/1jCFcLb9dVoQJWDOJgspr0A 
Extraction code: 69sc

illustrate:

• The downloaded dataset contains clean images and noisy images
  • Dataset directory: data
    • Original clear image directory: faces
      • train
      • val
    • Image directory with Gaussian noise added: GFaces
      • train
      • val

Tip: The procedure for adding Gaussian noise will be given in the following specific implementation.

3. Overall Design Ideas

(1) Generate model 

It adopts a network type similar to U-Net

 

(2) Discriminant model

4. Model denoising effect

 

(1) Demo demonstration effect

Tip: Here is a program (mainWindow.py) to display the image displayed by the generator, load the generator model saved after the previous training, and then use this model to generate a denoised image, as follows:

(1) Run mainWindow.py The initial interface is as follows

 

5. GitHub code download

https://github.com/KeepTryingTo/Python-

6. Model download

Link: https://pan.baidu.com/s/1KF-WLxhqCUIbUAQzq5sIag 
Extraction code: xiit