Python Image Processing Cookbook

How to do it...

We can now denoise using the denoising autoencoder in the following steps:

1. Define the to_img() and plot_sample_img() functions to convert a PyTorch tensor to an image from the lfw dataset (each grayscale image has a size of 50 x 37) and to save the image to disk, respectively:

def to_img(x):    x = x.view(x.size(0), 1, 50, 37)    return xdef plot_sample_img(img, name):    img = img.view(1, 50, 37)    save_image(img, './sample_{}.png'.format(name))

2. Define the add_noise() function to add random Gaussian noise to an image:

def add_noise(img):    noise = torch.randn(img.size()) * 0.2    noisy_img = img + noise    return noisy_img

3. Implement the min_max_normalization() and tensor_round() preprocessing functions to normalize and round a tensor, ...