Using wavelet decomposition to determine the dimension of structures from projected images

Mesoscale structures in turbulent media can often be described as fractional dimensional across a wide range of scales. The goal of this paper is to determine the structure's dimension from a projected image. Our method exploits the laws of scaling of wavelet power spectra under projection and does not carry any restrictions on the embedding and projected dimensions. We show that the wavelet power spectrum of a projected <i>γ</i> dimensional measure is [Formula: see text], where <i>j</i> is the wavelet scale. We contrast the wavelet method with the popular box-counting approach. For projected images, the use of box-counting at fixed thresholds often leads to erroneous results. We apply the method to James Webb Space Telescope (JWST) infrared and Chandra X-ray observations of the supernova remnant Cassiopeia A. We find that the emissions can be represented by projections of mesoscale substructures with fractal dimensions varying from [Formula: see text] for the warm CO layer observed by JWST, up to [Formula: see text] for the hot X-ray emitting gas layer in the supernova remnant.

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Journal
Proceedings of the National Academy of Sciences
Published
2026-03-23
DOI
https://doi.org/10.1073/pnas.2534122123
Primary Topic
Image and Signal Denoising Methods
Type
article
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article

Using wavelet decomposition to determine the dimension of structures from projected images

Svitlana Mayboroda, David N. Spergel
Proceedings of the National Academy of Sciences
Image and Signal Denoising Methods
article

Using wavelet decomposition to determine the dimension of structures from projected images

Svitlana Mayboroda, David N. Spergel
article en

Abstract

Mesoscale structures in turbulent media can often be described as fractional dimensional across a wide range of scales. The goal of this paper is to determine the structure's dimension from a projected image. Our method exploits the laws of scaling of wavelet power spectra under projection and does not carry any restrictions on the embedding and projected dimensions. We show that the wavelet power spectrum of a projected <i>γ</i> dimensional measure is [Formula: see text], where <i>j</i> is the wavelet scale. We contrast the wavelet method with the popular box-counting approach. For projected images, the use of box-counting at fixed thresholds often leads to erroneous results. We apply the method to James Webb Space Telescope (JWST) infrared and Chandra X-ray observations of the supernova remnant Cassiopeia A. We find that the emissions can be represented by projections of mesoscale substructures with fractal dimensions varying from [Formula: see text] for the warm CO layer observed by JWST, up to [Formula: see text] for the hot X-ray emitting gas layer in the supernova remnant.

Proceedings of the National Academy of SciencesVol. 123(13)
ETH Zurich (CH), Flatiron Health (United States) (US), Flatiron Institute (US), Toronto Metropolitan University (CA)
Openalex Percentile: Top 11%
Image and Signal Denoising Methods
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Using wavelet decomposition to determine the dimension of structures from projected images — Svitlana Mayboroda, David N. Spergel · Proceedings of the National Academy of Sciences (2026) | TGRS Research Map | TGRS