Image Processing Reference
In-Depth Information
%% Initialization
T1=P * W;
T2 = -W;
[A1,B1,C1,D1]=ssdata(T1);
[A2,B2,C2,D2]=ssdata(T2);
n1 = size(A1,1);
n2 = size(A2,1);
%% FIR filter to be designed
Aq = circshift(eye(N),-1);
Aq(N,1) = 0;
Bq = [zeros(N-1,1);1];
%% Semidefinite Programming
A = [A1, zeros(n1,n2), zeros(n1,N);
zeros(n2,n1), A2, zeros(n2,N);
zeros(N,n1),Bq * C2, Aq];
B = [B1;B2;Bq * D2];
NN = size(A,1);
X = sdpvar(NN,NN,'symmetric');
alpha_N1 = sdpvar(1,N);
alpha_0 = sdpvar(1,1);
gamma = sdpvar(1,1);
M1=A' * X * A-X;
M2=A' * X * B;
M3=B' * X * B-gamma;
C = [C1, alpha_0 * C2, alpha_N1];
D = D1 + alpha_0 * D2;
M = [M1, M2, C'; M2', M3, D; C, D, -gamma];
F = set(M < 0) + set(X > 0) + set(gamma > 0);
solvesdp(F,gamma);
%% Optimal FIR filter coefficients
q = fliplr([double(alpha_N1),double(alpha_0)]);
gmin = double(gamma);
7.2 Inverse FIR filtering by H norm
function [q,gmin] = inverseFIRhinf(P,W,N,n);
% [q,gmin]=inverseFIRhinf(P,W,N,n) computes the
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