Figure 8:

Illustration of the low rank (L) plus sparse (S) decomposition, which combines two models: the low rank and sparse, as x = s + l. Note the sparse component s can, actually, be sparse in a domain given by a sparsifying transform T, 2D wavelet in this case. If only the top 10% of the sparse coefficients are preserved to generate $\tilde{\mathit{s}}$ and the top 10% principal components are preserved to generate $\tilde{\mathit{l}}$, then a compressed $\tilde{\mathit{x}}=\tilde{\mathit{s}}+\tilde{\mathit{l}}$ can still represent x with very small difference $\tilde{\mathit{x}}-\mathit{x}$.