Science Score: 44.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: RoyCCWang
  • License: mpl-2.0
  • Language: Julia
  • Default Branch: main
  • Size: 1.17 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 2 years ago · Last pushed 9 months ago
Metadata Files
Readme License Citation

README.md

RieszDSP.jl

This is an implementation of the higher-order Riesz-wavelet transform for grid-sampled signals; i.e. the input is a multi-dimensional array like a grayscale image or video. The Riesz-wavelet is a perfect reconstruction transform, provided that a residual signal that the Riesz-wavelet transform does not operate on is saved.

A portion of the code here is based on the Generalized Riesz-Wavelet Toolbox for Matlab authored by Nicolas Chenouard, Dimitri Van De Ville and Michael Unser. I made modifications so that the wavelet subbands are not critically sampled.

Documentation

The documentation has further details and an image decomposition demo.

Quick demo: round-trip discrepancy

Apply the forward and inverse transform and compare against the original input, y.

```julia import RieszDSP as RZ using LinearAlgebra

generate input data.

T = Float64 y = randn(T, 45, 512, 3)

specification for the number of wavelet subbands.

N_scales = round(Int, log2( maximum(size(y))))

forward transform.

WRY, residual = RZ.rieszwaveletanalysis(y, N_scales)

residual is the portion that does not under go the Riesz-wavelet transform.

inverse transform.

yr = RZ.rieszwaveletsynthesis(WRY, residual)

this is a perfect reconstruction (up to numerical precision), if residual is kept.

println("relative discrepancy between y and yr: ", norm(y-yr)/norm(y) ) println() ```

Owner

  • Name: Roy Wang
  • Login: RoyCCWang
  • Kind: user

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Wang"
  given-names: "Roy Chih Chung"
  orcid: "https://orcid.org/0000-0002-1391-4536"
title: "RoyCCWang/RieszDSP.jl"
version: 0.2.2
date-released: 2024-01-10
url: "https://github.com/RoyCCWang/RieszDSP.jl"

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