Algorithm 1.

K-SVD Dictionary Construction

1:	Initialize $D_0^g$ (T0 resp.)
2:	repeat
3:	Find sparse coefficients Γ (γi’s) using any pursuit algorithm.
4:	for j = 1, …, d0, update fj, the j-th column of $D_0^g$ (T0 resp.), by the following process do
5:	Find the group of vectors that use this atom: ζj:= {i : 1 ≤ i ≤ d0, γi(j) ≠ 0}
6:	Compute $E_j:=Q-{\sum }_{i\ne j}{\mathit{f}}_i{\mathrm{\Gamma }}_T^i$ where ${\mathrm{\Gamma }}_T^i$ 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 $E_j^R=U\mathrm{\Delta }V$
9:	fj is updated with the first column of U
10:	The non-zeros elements in ${\mathrm{\Gamma }}_T^j$ is updated with the first column of V × Δ(1, 1)
11:	end for
12:	until Convergence criteria is met