Compressed Sensing
A mathematical theory that exploits signal sparsity to accurately reconstruct signals from far fewer samples than required by the Nyquist sampling theorem. The theory has been applied to the domain of model compression.
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Compressed sensing is one of the theoretical foundations of single-pixel imaging; it points out that if a signal is sparse in some transform domain, it can be recovered with far fewer measurements than required by the Nyquist sampling theorem.
Recovery guarantees for compressed sensing usually rely on the restricted isometry property (RIP) of the observation matrix and on the noise level; when fault density rises or the noise is not independent white noise, recovery performance degrades significantly.
压缩感知的核心思想是：若信号在某个基下稀疏，则可用远少于奈奎斯特采样定理所要求的测量次数精确重构信号
All Facts (3)
Compressed sensing is one of the theoretical foundations of single-pixel imaging; it points out that if a signal is sparse in some transform domain, it can be recovered with far fewer measurements than required by the Nyquist sampling theorem.
50%UnverifiedRecovery guarantees for compressed sensing usually rely on the restricted isometry property (RIP) of the observation matrix and on the noise level; when fault density rises or the noise is not independent white noise, recovery performance degrades significantly.
50%Unverified压缩感知的核心思想是：若信号在某个基下稀疏，则可用远少于奈奎斯特采样定理所要求的测量次数精确重构信号
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