Python Image Processing Cookbook

How to do it...

Denoising different types of noises in an image is done with the following steps:

1. Define the plt_hist() function to plot the histogram of the noise added to the image:

def plt_hist(noise, bins=None):    plt.grid()    plt.hist(np.ravel(noise), bins=bins, alpha=0.5, color='green')    plt.tick_params(labelsize=15)    plt.title('Noise Historgram', size=25)

2. Next, define the plt_images() function to plot all the images, that is, the original, noisy and denoised images, using the mean/median filters, and plot the noise histogram by calling the plt_hist() function defined previously. Also, compare the quality of the images denoised using the filters with the PSNR:

def plt_images(im, im_noisy, noise, noise_type, i): im_denoised_mean = uniform_filter(im_noisy, ...