# Here begins the algorithm
# whitening processing. It's important
X = whiten_projection(mixed_sources)
(sdr_ref, sir_ref, sar,
perm) = mmetrics.bss_eval_sources(np.asarray(sources), np.asarray(X))
print('The mean value of the reference SDR is: ', np.mean(sdr_ref))
reference_sdr.append(np.mean(sdr_ref))
Se = np.copy(X)
Se_old = np.copy(Se)
for it in np.arange(max_it):
# 1. denoising
Se = Dic_proj_double(Se_old, num_coeff, sigma)
# 2. get demixing matrix
WW = get_demix(X, Se)
# 3. whiten the demix matrix
WW = whiten_projection(WW)
# 4. get the new source
Se = np.dot(WW, X)
if np.linalg.norm(Se - Se_old, ord='fro') < 1e-6:
print('Dict demix convergence reached at iteration', it)
break
if it % 5 == 0:
(sdr_temporary_i, sir, sar,
perm) = mmetrics.bss_eval_sources(np.asarray(sources), Se)
sdr_temporary.append(np.mean(sdr_temporary_i))
(sdr, sir, sar, perm) = mmetrics.bss_eval_sources(np.asarray(sources), Se)
ssim_estimated_source1 = []
ssim_estimated_source2 = []
estimated_matrix = []
mean_ssim = []
for it in np.arange(max_it):
# 1. denoising
if method == 'TV':
Se = TV_proj(Se_old, sigma)
if method == 'bm3d':
Se = bm3d_proj(Se_old, sigma)
if method == 'non-local':
Se = Nonlocal_proj(Se_old, sigma)
# 2. get demixing matrix
WW = get_demix(X_i, Se)
# 3. whiten the demix matrix
WW, W = whiten_projection(WW)
# 4. get the new source
Se = np.dot(WW, X_i)
if np.linalg.norm(Se - Se_old, ord = 'fro') < 1e-6:
print('Dict demix convergence reached at iteration', it)
#break
if it%5 == 0:
print(it)
(sdr_temporary_i, sir, sar, perm) = mmetrics.bss_eval_sources(np.asarray(source_channel_list_hsv[i]), Se)
ssim1, ssim2 = SSIM(source_channel_list_hsv[i], Se, image_size, permutation, X_normalized, B_i)
ssim_estimated_source1.append(ssim1)