Figure 5:

Example of the sparsity or compressibility of the knee images in the 3D wavelet (2D+time) transformed domain. The original images x are sparse when transformed via Tx, such as wavelet transform. If only the top 15% of the coefficients are preserved, and the rest nulled, we can still recover a very similar image $\tilde{\mathit{x}}$ using inverse transform T⁻¹. The difference $\tilde{\mathit{x}}-\mathit{x}$ is usually very small and has a noise-like aspect.