for i, s in enumerate(s_arr):
for j, m in enumerate(m_arr):
print(j)
for k, sigma in enumerate(var_arr):
for l in range(num_trials):
indices = np.random.choice(n, s, replace=False)
theta = np.zeros((n, 1))
theta[indices, :] = np.random.randn(s, 1)
theta = theta / np.linalg.norm(2)
A = np.random.rand(m, n)
y = A @ theta + sigma * np.random.randn(m, 1)
abs_tol = math.sqrt(4 * n) * sigma * np.linalg.norm(theta)
theta_recon = GRASP(A, y, s, abs_tol)
# print(rmse(theta, theta_recon))
results[i, j, k, l] = rmse(theta, theta_recon)
results_median = np.median(results, axis=-1)
for i, s in enumerate(s_arr):
plt.figure()
plt.title("Sparsity level: " + str(s))
im = plt.imshow(results_median[i, :, :])
ax = plt.gca()
ax.set_xticks(np.arange(len(var_arr)))
ax.set_yticks(np.arange(len(m_arr)))
# ... and label them with the respective list entries.
ax.set_xticklabels(var_arr * 100)
ax.set_yticklabels(m_arr)
ax.set_xlabel("Relative Intensity of Noise")
ax.set_ylabel("Number of measurements")
for i2 in range(len(m_arr)):
data_path = "./data/"
sigma = 0.01
img_array = cv2.imread(data_path + "clown.bmp", 0)
plt.figure()
plt.title("Original Image")
plt.imshow(img_array, cmap='gray')
plt.show()
assert img_array.shape[0] == img_array.shape[1]
n = img_array.shape[0]
Phi = getDCTBasis(n)
m = 300
theta = Phi.T @ img_array # Id DCT of columns
theta_recon = np.zeros((n, n))
for i in range(n):
print(i)
col = np.expand_dims(theta[:, i], axis=1)
A = np.random.rand(m, n) @ Phi
y = A @ col + sigma * np.linalg.norm(col) * np.random.randn(m, 1)
abs_tol = math.sqrt(4 * n) * sigma * np.linalg.norm(col)
col_recon = GRASP(A, y, 100, abs_tol)
theta_recon[:, i] = col_recon.squeeze()
img_recon = Phi @ theta_recon # 1D IDCT of cols
plt.figure()
plt.title("Reconstructed Image")
plt.imshow(img_recon, cmap='gray')
plt.show()
print("Image reconstruction error: ", rmse(theta, theta_recon))