Hands-On Image Processing with Python

Half-toning

One way to reduce the false contour artefacts in thresholding (binary quantization) is to add uniformly distributed white noise to the input image prior to quantization. Specifically, to each input pixel of the grayscale image, f(x, y), we add an independent uniform [-128,128] random number, and then the thresholding is done. This technique is called half-toning. The following code block shows an implementation:

im = Image.open('../images/swans.jpg').convert('L')im = Image.fromarray(np.clip(im + np.random.randint(-128, 128, (im.height, im.width)), 0, 255).astype(np.uint8))pylab.figure(figsize=(12,18))pylab.subplot(221), plot_image(im, 'original image (with noise)')th = [0, 50, 100, 150, 200]for i in range(2, 5): im1 = im.point(lambda ...