Calculating Particle Spacing in a Lattice Using Fourier Transform

In summary, the conversation discusses applying a Fourier Transform to an image of particles forming a lattice to find the average distance between the particles. The presence of an airy pattern and the distance to the first ring are mentioned as potential indicators of the average distance. The individual is unsure of how to convert the reciprocal space pixel size to normal space and proposes using 2pi/d, where d is the spacing between particles, but is uncertain of the number of pixels in 2pi/d. The concept of an Airy disk and aliases as a means of finding the lattice spacing is also brought up.
  • #1
indie452
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Homework Statement



Okay so i am applying a FT to an image of particles that are forming a lattice, and i need to find the average distance between the particles

because its not a perfect lattice, I am getting an airy pattern and i believe that the distance to the first ring is the average distance between the particle.

But, i don't know how to convert reciprocal space pixel size into normal space.
I believe i need to use the 2pi/d where d is the spacing between 2 particles, but i don't know how many pixels are in 2pi/d

note: i know the scale of my image width and height in pixels and microns
 
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  • #2
An Airy disk results from transforming a circular disk. Are your particles contained within a disk? You may be looking at the envelope.

Is your lattice regular (e.g., rectangular, triangular, etc.)? If so, then you can find the spacing from the location of aliases, that is, replications of the primary Airy disk.
 

1. What is a Fourier transform on an image?

A Fourier transform on an image is a mathematical operation that converts a 2-dimensional image into its frequency domain representation. This allows for the analysis of the image's spatial frequency components, which can reveal important information about the image's features and patterns.

2. How does a Fourier transform on an image work?

A Fourier transform on an image works by decomposing the image into its constituent spatial frequency components using complex numbers and trigonometric functions. Essentially, the transform takes the image's pixel values and converts them into a series of sinusoidal waves with different amplitudes and frequencies.

3. What is the purpose of performing a Fourier transform on an image?

The purpose of performing a Fourier transform on an image is to analyze the image's frequency content. This can help in tasks such as image enhancement, noise reduction, and feature detection. It is also commonly used in image compression techniques.

4. What are some applications of Fourier transform on images?

Fourier transform on images has a wide range of applications in various fields, including signal processing, computer vision, and medical imaging. Some specific examples include image denoising, image registration, and image segmentation.

5. Are there any limitations to using Fourier transform on images?

While Fourier transform on images is a powerful tool, it does have some limitations. For example, it assumes that the image is infinite and periodic, which may not be true for real-world images. Additionally, it can be sensitive to noise and may not be suitable for all types of image analysis tasks.

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