November 2018
Intermediate to advanced
492 pages
12h 19m
English
Content preview from Hands-On Image Processing with Python
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Summary
In this chapter, we discussed a few important concepts primarily related to 2D convolution and its related applications in image processing, such as filtering in the spatial domain. We also discussed a few different frequency domain filtering techniques and illustrated them with quite a few examples with the scikit-image numpy fft, scipy, fftpack, signal, and ndimage modules. We started with the convolution theorem and its application in frequency domain filtering, various frequency domain filters such as LPF, HPF, and notch filters, and finally deconvolution and its application in designing filters for image restoration, such as inverse and Wiener filters.
On completion of this chapter, the reader should be able to write Python code ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.