November 2018
Intermediate to advanced
492 pages
12h 19m
English
Content preview from Hands-On Image Processing with Python
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Deconvolution and inverse filtering with FFT
Given a blurred image with a known (assumed) blur kernel, a typical image processing task is to get back (at least an approximation of) the original image. This particular task is known as deconvolution. One of the naive filters that can be applied in the frequency domain to achieve this is the inverse filter that we are going to discuss in this section. Let's first start by blurring the grayscale lena image with Gaussian blur using the following code:
im = 255*rgb2gray(imread('../images/lena.jpg'))gauss_kernel = np.outer(signal.gaussian(im.shape[0], 3),signal.gaussian(im.shape[1], 3))freq = fp.fft2(im)freq_kernel = fp.fft2(fp.ifftshift(gauss_kernel)) # this is our Hconvolved = freq*freq_kernel ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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