I am starting with the following image:

For the curious, it is an STM scanned image of graphite. I am hoping to find some kind of periodic structure in the image related to the honeycomb structure of graphite.I followed the instructions in this post in how to do the 2D Fourier transform and look at the magnitude spectrum of the image. I cropped the center part (ignoring the axes and color information) out and analyzed that picture to end up with:

Now, I can see the kind of hexagonal structure I want to see, and I want to filter out some of the other bright areas. This leads me to my two main questions:
- Does mathematica allow for easy band-pass filtering? I need to filter out the low and high frequency noise in the image. I can do it manually if necessary.
- How do I transform the image back from the magnitude spectrum to the regular image? My hope is that, after filtering, the structure will be more apparent and visible when converted back to the traditional image. The color-coding for height is irrelevant for my purposes.
EDIT: I also have access to the original data that composes my image. It comes in a list of x,y,z pairs where the z coordinate is the relative height used for color coding:
0.000,0.000,0.134
0.000,1.000,0.134
0.000,2.000,0.132
0.000,3.000,0.133
0.000,4.000,0.136
0.000,5.000,0.131
0.000,6.000,0.132
0.000,7.000,0.130
0.000,8.000,0.133
BandpassFilterin the docs? https://reference.wolfram.com/language/ref/BandpassFilter.html? – dr.blochwave Apr 26 '15 at 19:25Fourierto get the complex-valued result consisting of magnitude and phase information. Zero out any parts of it that you don't like. Then, return to real space usingInverseFourier. But, I agree with Simon: if this picture is your source data, it is a fool's errand (it has been downsampled to a ridiculous extent). Use the original STM data instead. – Oleksandr R. Apr 26 '15 at 20:49