# Fourier transform of Image to identify sinusoidal sources of interference

I'm a statistics grad student, and I just started getting into Digital-Image-Processing (an analogy for processing super-large contingency tables). In the book "Digital Image Processing" by Gonzalez and Woods, I was reading chapter 2, page 94 of the third edition, and found the following image that is MAGIC to me...

They begin with a noisy image, identify some 'hot points' surrounding the center of the Fourier transform, then simply remove them, reverse the transform, and I can't help vocally demonstrate an emotional reaction while assessing that the image is now crystal clear.

Does anyone have any insight into this? I'd like to know what are the underlying notions and methods used to filter this image.

Additionally, if any of the StackExchange users a familiar with this topic and can suggest some articles/books that I might read, I would be very grateful.

• Those points on fig (b) displayed in a circle are the Fourier's transform of the sinusoidal parasitic components. The mask fig (c) aim to remove them, therefore, the final image fig (d) is fig (a) minus the sinusoidal noise. it works exactly like 1D signal processing but ... in 2D – MaximGi Feb 26 '16 at 8:25
• I don't think that "HOW AWESOME" and "POW!" is an example of scientific language... Any way to refine the question? – jojek Feb 26 '16 at 8:50
• I believe that the question deserves some improvement time since it triggers interesting questions on the magic of image or signal processing, and the choice of stunning examples – Laurent Duval Feb 26 '16 at 10:16
• on hold ?? I was in the middle of an answer. He is asking for a deeper explanation of this example (not opinion based), and, additionally some references (can be opinion based, but usually done in the comments). – MaximGi Feb 26 '16 at 13:25
• @jojek sorry if my use of language is overly casual... I was just trying to convey my enthusiasm (and hopeful stir up some enthusiasm from any young readers that came across my post). – Lewkrr Feb 29 '16 at 23:08