Interpreting 2-D FFTs of images

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In summary, the conversation discusses the use of FFTs on electron microscope images to extract quantitative values for characterizing crystallinity and comparing it between samples. The easier question is about the information that can be extracted from 2D FFTs of the structures, while the more difficult question is about comparing FFTs between different images. The expert suggests that the more ordered the structure, the better defined the Fourier peaks will be and the FT can also reveal the type of symmetry present. When comparing FFTs, normalizing the integrated power is important. The speaker also asks for suggestions and mentions having access to the original images.
  • #1
Hyo X
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I have electron microscope images of structures of varying periodicity, ranging from highly crystalline to highly amorphous. It is relatively straightforward to take FFTs of these images, but what i want to do is extract a quantitative value to characterize crystallinity and compare it between samples.

The easier question is, what kind of information can I extract from 2D FFTs about the structures?

The more difficult question is: how can I quantitatively compare FFTs between different images?

I have some ideas, but would appreciate suggestions. These are two example FFTs.

Image1
116A_33Hex_01_FFT.jpg

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Image 2
116C_33IPA_30_FFT.jpg
 
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  • #2
The more highly ordered the structure, the better defined the Fourier peaks, so you can conclude for example that the lower image is from a more amorphous structure (though there is a slight amount of order due to the "ghost" rings on the y-axis).

The FT will also tell you what sort of symmetry is present - e.g. sixfold symmetry typically indicates a hexagonal-packed structure. The number of visible harmonics indicates the "fidelity" of the symmetry.

When comparing FFTs, be sure to normalise the integrated power.

What are your ideas? Presumably you have access to the actual (non Fourier-Transformed) images.

Claude.
 

What is a 2-D FFT of an image?

A 2-D FFT (Fast Fourier Transform) of an image is a mathematical transformation that converts a 2-dimensional spatial image into its corresponding frequency domain representation. This allows for the analysis of the image in terms of its frequency components.

Why is it important to interpret 2-D FFTs of images?

Interpreting 2-D FFTs of images can provide valuable insights into the underlying structure and patterns present in the image. It can also aid in tasks such as image filtering, compression, and feature extraction.

How does a 2-D FFT of an image differ from a 1-D FFT?

A 2-D FFT of an image takes into account both the horizontal and vertical frequencies present in the image, while a 1-D FFT only considers the horizontal frequency. This allows for a more comprehensive analysis of the image.

What are some common applications of interpreting 2-D FFTs of images?

Interpreting 2-D FFTs of images is commonly used in fields such as image processing, computer vision, and signal processing. It is also applied in various industries such as medical imaging, remote sensing, and astronomy.

Are there any limitations to interpreting 2-D FFTs of images?

One limitation of interpreting 2-D FFTs of images is that it assumes the image is periodic and can result in artifacts if this is not the case. Additionally, the interpretation of the frequency components can be complex and may require advanced mathematical knowledge.

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