f = np.linspace(0, Fs, Ns) k1 = 2 k2 = 4 k3 = 6 k4 = 100 R = 4 alpha = 1e-6 M = np.size(y) N = int(M * R) ff = np.linspace(0, Fs, int(Ns * R)) A = pys.gaussian((M, N)) A = pys.odctdict((M, N), isnorm=True) # A = pys.dctdict(N) # A = pys.odctndict((M, N)) print(A.shape) x1 = pys.romp(y, A, k=k1, alpha=alpha, verbose=False) y1 = np.matmul(A, x1) x2 = pys.romp(y, A, k=k2, alpha=alpha, verbose=False) y2 = np.matmul(A, x2) x3 = pys.romp(y, A, k=k3, alpha=alpha, verbose=False) y3 = np.matmul(A, x3) x4 = pys.romp(y, A, k=k4, alpha=alpha, verbose=False)
sfrom = [0, 255]
# sfrom = None
sto = [0, 255]
alpha = 0.000001
CR = 4
N = H
M = int(N / CR)
k1 = int(N / 8)
dictype = 'DCT'
mestype = 'Gaussian'
seed = 2019
if mestype is 'Gaussian':
Phi = pys.gaussian((M, N), seed=seed, verbose=True)
print("===observation...")
if dictype is 'DCT':
D = pys.dctdict(N)
A = np.matmul(Phi, D)
plt.figure()
plt.subplot(221)
plt.imshow(X)
plt.title('Orignal image signal')
plt.subplot(222)
plt.imshow(D)
plt.title('Dictionary matrix (' + dictype + ')')
alpha = 0.000001
CR = 16
N = H
M = int(N / CR)
k1 = int(N / 32)
k2 = int(N / 16)
k3 = int(N / 8)
k4 = int(N / 4)
k5 = int(N / 2)
k6 = int(N / 1)
dictype = 'DCT'
mestype = 'Gaussian'
if mestype is 'Gaussian':
Phi = pys.gaussian((M, N), verbose=True)
print("===observation...")
if dictype is 'DCT':
D = pys.dctdict(N)
A = np.matmul(Phi, D)
plt.figure()
plt.subplot(221)
plt.imshow(X)
plt.title('Orignal image signal')
plt.subplot(222)
plt.imshow(D)
plt.title('Dictionary matrix (' + dictype + ')')