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 to do 2D convolution/filtering, and should also be able to write Python code to implement time/frequency domain filters with/without convolution.

In the next chapter, we will start on different image enhancement techniques based on the concepts introduced in the last two chapters.