标题： 基于提升小波的稀疏波场重建与去噪 显示英文标题

标题： Sparse wavefield reconstruction and denoising with boostlets

摘要： Boostlets 是时空函数，它们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 的稀疏性特性，并发现其分解结果比其他最先进的表示系统（如小波和剪切波）显著更稀疏。 这在对 Boostlet 系数进行硬阈值处理时转化为改进的去噪性能。 结果表明，Boostlets 为在统一时空中稀疏分解波场提供了一个自然的框架。 显示英文摘要

摘要： 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.