boostlets能更稀疏地表示波场，提升去噪效果。
Sparse wavefield reconstruction and denoising with boostlets
- 用放大、双曲旋转和平移参数化波形，实现时空统一表示
- 相比小波和剪切波，分解结果稀疏度提升显著
- 适合需要高效去噪的地震、声学等波场处理场景
Boostlets 是一类时空函数，可将非色散波场分解为一组由缩放、双曲旋转和平移参数化的局部波形。我们研究了 boostlets 的稀疏性，发现其分解结果显著优于当前最先进的表示系统（如小波和剪切波）。在对 boostlet 系数进行硬阈值处理时，该稀疏性带来了更优的去噪性能。结果表明，boostlets 为在统一时空域中稀疏表示波场提供了一个自然框架。
原文摘要 · Abstract (English)
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.
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