Algorithm 1.

K-SVD Dictionary Construction

1:	Initialize F
2:	repeat
3:	Find sparse coefficients Γ (γi’s) using any pursuit algorithm.
4:	for j = 1,…, M, update fj, the j-th column of F, by the following process do
5:	Find the group of vectors that use this atom: ζj := {i : 1 ≤ i ≤ M, γi(j) ≠ 0}
6:	Compute Ej := Q − ∑i≠j fi ${\mathrm{\Gamma }}_T^i$ where ${\Gamma }_T^i$ is the i-th row of Γ
7:	Extract the i-th columns in Ej, where i ∈ ζj, to form $E_j^R$
8:	Apply SVD to get $E_j^R$ = UΔV
9:	fj is updated with the first column of U
10:	The non-zeros elements in ${\Gamma }_T^i$ is updated with the first column of V × Δ(1, 1)
11:	end for
12:	until Convergence criteria is met