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2.6. Algorithm Summary
The following pseudo codes give further clarification of the specific implementation
of the proposed algorithm for an input noisy image:
Algorithm 1 Pseudocode of the Proposed Algorithm
Input: Y M×N , P,L and L s .
Output: Ŝ f
for each x = 1 to M ×N do
/∗Convert the pixel-based image to the patch-based image ∗/
H(x,1 : P) ← Y
(
m+1 : √ P,n+1 : √ P
end for
/∗The SVD-based low-rank approximation using parallel analysis ∗/
Ŝ t = zeros(M ×N,P); W t = zeros(M ×N,P);
for each x = 1 to M ×N do
Ψ x ← L; Y x = H(x,:); Y Ψx = H(Ψ x ,:);
Ψ s x = BlockMatching(Y x,Y Ψx ,L s );
Zx υ = H(Ψs x ,:); M x = mean(Zx υ);
¯Z x υ = Zυ x −M x; (¯Zυ )T
x
¯Zυ x = VΣ 2 V T ; C T C = VΛ 2 V T ;
λ = diag(Σ); α = diag(Λ);
K = max{p = 1,··· ,P|λ p ≥ α p }; /∗Parallel analysis ∗/
Ẑ x = U K Σ K VK T +M x;
Ŝ t (Ψ s x ,:) = Ŝt (Ψ s x ,:)+W xẐx;
W t (Ψ s x ,:) = Wt (Ψ s x ,:)+W x;
end for
I = zeros
(
M + √ P −1,N + √ P −1
)
;
)
; Q = I;
/∗The weighted averaging of the aggregate estimates of each pixel ∗/
for each a,b = 1 to √ P do
id = (b−1)× √ P +a;
Π a = a : M +a−1; Π b = b : N +b−1; )
I(Π a ,Π b ) = I (Π a ,Π b )+reshape(Ŝt (:,id),[M,N] ;
Q(Π a ,Π b ) = Q(Π a ,Π b )+reshape(W t (:,id),[M,N]);
end for
Ŝ t = I/Q; )
Ŝ f = WienerFiltering(Ŝt ; /∗The empirical Wiener filtering ∗/
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