Result: Sparse wavefield reconstruction and denoising with boostlets

Title:
Sparse wavefield reconstruction and denoising with boostlets
Authors:
Zea, Elias, Laudato, Marco, Andén, Joakim
Source:
2025 International Conference on Sampling Theory and Applications (SampTA). :1-5
Publication Status:
Preprint
Publisher Information:
IEEE, 2025.
Publication Year:
2025
Subject Terms:
FOS: Computer and information sciences, Sound (cs.SD), Beräkningsmatematik, wavefields, Signalbehandling, Fluid Mechanics, multi-scale representations, sparse reconstruction, Strömningsmekanik, Computer Science - Sound, Computational Mathematics, Audio and Speech Processing (eess.AS), Signal Processing, denoising, FOS: Electrical engineering, electronic engineering, information engineering, boostlets, Electrical Engineering and Systems Science - Audio and Speech Processing
Document Type:
Academic journal Article<br />Conference object
File Description:
application/pdf
DOI:
10.1109/sampta64769.2025.11133531
DOI:
10.48550/arxiv.2502.08230
Access URL:
http://arxiv.org/abs/2502.08230
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-369265
Rights:
STM Policy #29
CC BY
Accession Number:
edsair.doi.dedup.....12bacf3375209aebfbd6d1c494c61ccf
Database:
OpenAIRE

Further Information

Boostlets are spatiotemporal functions that decompose nondispersive wavefields into a collection of localized waveforms parametrized by dilations, hyperbolic rotations, and translations. We study the sparsity properties of boostlets and find that the resulting decompositions are significantly sparser than those of other state-of-the-art representation systems, such as wavelets and shearlets. This translates into improved denoising performance when hard-thresholding the boostlet coefficients. The results suggest that boostlets offer a natural framework for sparsely decomposing wavefields in unified space-time.
5 pages, 4 figures