Spherical Harmonic Decomposition of an image

In summary, the conversation discusses the concept of decomposing an image, specifically the WMAP data of the CMB, using spherical harmonics. This is similar to Fourier decomposition of functions and involves analyzing the frequency of certain spectra to understand the level of isotropy in a given region.
  • #1
FunkyDwarf
489
0
Hey guys,

Homework Statement


Basically the question is to explain the meaning of a SHD of an image, specifically the image of the WMAP data of the CMB. Now i understand decomposition when it comes to functions but I am not sure how to extend that to images. Is it simply that you are analysing the frequency of certain spectra and mapping that? I say that because we are supposed to explain the significance of the peaks in the decomposition of the WMAP data and i assumed that they would give information of the level of isotropy in that region.

Cheers
-Z
 
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  • #2
If the "image" in question is a collection of data plotted on the surface of a sphere, then it can be decomposed via spherical harmonics. This is basically a spherical analogue of Fourier decomposition of functions.

Essentially, what you do is start with a function on the sphere whose value at a particular theta, phi is equal to the data point there. Then you find the spherical harmonic series that represents this function.
 

What is spherical harmonic decomposition of an image?

Spherical harmonic decomposition is a mathematical technique used to represent an image as a combination of spherical harmonic functions. These functions are used to describe the variations in intensity and color across the image.

Why is spherical harmonic decomposition used?

Spherical harmonic decomposition is used to simplify the representation of an image and make it easier to analyze. It also helps to reduce the amount of data needed to store the image, making it more efficient for processing and transmission.

How does spherical harmonic decomposition work?

Spherical harmonic decomposition involves breaking down an image into smaller sections and approximating each section with a spherical harmonic function. These functions are then combined to create a representation of the entire image.

What are the benefits of using spherical harmonic decomposition?

Aside from simplifying image representation and reducing data storage, spherical harmonic decomposition can also help with tasks such as image compression, noise reduction, and feature detection. It can also be used in applications such as virtual reality and computer graphics.

Are there any limitations to spherical harmonic decomposition?

One limitation of spherical harmonic decomposition is that it is most effective for representing images with smooth, continuous variations in intensity and color. Images with sharp edges or high frequency features may not be accurately represented using this technique.

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