Better Edge detection and Noise reduction in images using Fourier Transform
Date Thu 13 September 2018 Tags Python / Image Processing
This is the continuation of my previous blog where we learned, what is fourier transform and how application of high pass filter on fourier transform of an image can potentially help us with edge detection. In case you missed it, please find it here :
Edge detection in images using Fourier Transform
In this post we are gonna learn about two other types of filters and see how can they help us in image analysis :
- Low pass filter
- Band pass filter
Filters
I’ll quickly go over the the concept of filters in the context of Fourier transform again. I am reproducing it as is from my last post here :
Filters in image processing are just what the name suggests, Filter. They are typically a mask array of the same size as the original image which when superimposed on the ordinal image, extracts only the attributes that we are interested in.
As mentioned earlier, in an FFT transformed image, low frequencies are found in the center and high frequencies are scattered around, we can then create a mask array which has a circle of zeros in the center and rest all ones. Now when this mask is applied to the original image, the resultant image would only have high frequencies. This becomes quite useful as low frequencies correspond to edges…