https://github.com/nclack/haar

nD haar transform and it's inverse.
https://github.com/nclack/haar

Last synced: 2 months ago
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nD haar transform and it's inverse.

• Host: GitHub
• URL: https://github.com/nclack/haar
• Owner: nclack
• License: mit
• Created: 2012-03-26T02:56:20.000Z (almost 13 years ago)
• Default Branch: master
• Last Pushed: 2012-03-27T22:18:43.000Z (almost 13 years ago)
• Last Synced: 2023-03-11T18:16:52.433Z (almost 2 years ago)
• Language: C
• Homepage: http://nclack.github.com/haar/
• Size: 293 KB
• Stars: 5
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README
  • License: LICENSE

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README

        
# Haar

nD [Haar][2] transform and it's inverse.

## Introduction

I wanted to play around with wavelet based blending and multi-scale

representations in the data I work with, which is often a 3d-scalar field.

I also wrote a [z-order][1] transform thinking it would be fun to implement the

haar transform in a cache oblivious way.  The z-ordering gives the data a

hierarchical structure in memory that helps coallesce memory access in a 

recursive algorithm, like the one commonly used to implement wavelet 

transforms.  I haven't gotten around to actually use the z ordering.

There's been a gap of time between when I wrote the code and wrote this README.

I think the algorithms are possibly in place (this may be untested?), with a

little bit of extra memory used for book-keeping.  I believe the dimensions of

the input must be powers of two, but the dimensions don't all need to be the

same.  You can also pass in, or output to, a subvolume of an nd-array.

The next thing I want to do is do a GPU implementation, maybe do some other

wavelets, and then actually use the code for something (good, not evil).

[1]: http://en.wikipedia.org/wiki/z-order_curve

[2]: http://en.wikipedia.org/wiki/Haar_wavelet