recon_sart_30 = SartReconstructor('SART30', sart_n_iter=n_iter, sart_relaxation=0.30)
recon_sart_15 = SartReconstructor('SART15', sart_n_iter=n_iter, sart_relaxation=0.15)
recon_sart_07 = SartReconstructor('SART07', sart_n_iter=n_iter, sart_relaxation=0.07)
recon_sart_05 = SartReconstructor('SART05', sart_n_iter=n_iter, sart_relaxation=0.05)
recon_sart_03 = SartReconstructor('SART03', sart_n_iter=n_iter, sart_relaxation=0.03)
recon_sart_02 = SartReconstructor('SART02', sart_n_iter=n_iter, sart_relaxation=0.02)
recons = [
recon_fbp,
recon_sart_95,
recon_sart_90,
recon_sart_80,
recon_sart_50,
recon_sart_30,
recon_sart_15,
recon_sart_07,
recon_sart_05,
recon_sart_03,
recon_sart_02,
]
imgs = [ r.calc(sinogram, theta) for r in recons ]
for r in recons:
mse, psnr, ssim = r.eval(gt)
print( "{}: MSE:{:.5f} PSNR:{:.5f} SSIM:{:.5f}".format(
r.name, mse, psnr, ssim
))
plot_grid( [gt]+imgs, FOCUS=FOCUS, save_name='art.png', dpi=500 )
sart_relaxation=0.15)
recon_dip = DgrReconstructor(
'DGR',
dip_n_iter=4001,
net='skip',
lr=0.01,
reg_std=1. / 100,
w_proj_loss=1.00,
# w_perceptual_loss=0.01,
w_tv_loss=0.00,
w_ssim_loss=0.00)
img_sart = recon_sart.calc(sinogram, theta)
recon_dip.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log/')
img_dip = recon_dip.calc(sinogram, theta)
recons = [
recon_sart,
recon_dip,
]
for r in recons:
mse, psnr, ssim = r.eval(gt)
print("Noise 25 {}: MSE:{:.5f} PSNR:{:.5f} SSIM:{:.5f}".format(
r.name, mse, psnr, ssim))
plot_grid([gt, img_sart, img_dip],
FOCUS=FOCUS,
save_name='noise30.png',
dpi=500)
recon_dip1.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log')
img_dip1 = recon_dip1.calc(sinogram, theta)
recon_dip2.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log')
img_dip2 = recon_dip2.calc(sinogram, theta)
recon_dip3.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log')
img_dip3 = recon_dip3.calc(sinogram, theta)
recons = [recon_fbp, recon_sart, recon_sart_tv,
recon_dip1, recon_dip2, recon_dip3]
for r in recons:
mse, psnr, ssim = r.eval(gt)
print( "{}: MSE:{:.5f} PSNR:{:.5f} SSIM:{:.5f}".format(
r.name, mse, psnr, ssim
))
for i, img in enumerate([gt, img_fbp, img_sart, img_sart_tv, img_dip1, img_dip2, img_dip3,]):
plot_grid([img],
FOCUS=None, save_name=res_name+'_'+str(i)+'_all.png', dpi=500
)
plot_grid([
gt, img_fbp, img_sart, img_sart_tv,
img_dip1, img_dip2, img_dip3,],
FOCUS=None, save_name=res_name+'_all.png', dpi=500
)
# gt, sinogram, theta, FOCUS = image_to_sparse_sinogram(fname, channel=1,
# n_proj=128, size=512, angle1=0.0, angle2=180.0, noise_pow=12.0 )
# plt.figure()
# plt.imshow(sinogram, cmap='gray')#, vmin=0.0, vmax=1.0)
# plt.figure()
# plt.imshow(gt, cmap='gray', vmin=0.0, vmax=1.0)
# plt.figure()
# plt.imshow(iradon(sinogram, theta=theta), cmap='gray')
# plt.show()
recon_fbp = IRadonReconstructor('FBP')
fbp_img = recon_fbp.calc(sinogram, theta)
imgs = [
gt,
fbp_img,
gt,
fbp_img,
gt,
fbp_img,
]
# plot_grid(
# imgs, FOCUS=None, show=True, number_of_rows=3
# )
# plot_grid(
# imgs, FOCUS=FOCUS, show=True, number_of_rows=3
# )
plot_grid(imgs, ZOOM=FOCUS, show=True, number_of_rows=1, plot1d=50)
SartBM3DReconstructor('SART+BM3Dsigma0.10',
sart_n_iter=n_iter,
sart_relaxation=0.15,
bm3d_sigma=0.10),
SartBM3DReconstructor('SART+BM3Dsigma0.20',
sart_n_iter=n_iter,
sart_relaxation=0.15,
bm3d_sigma=0.20),
SartBM3DReconstructor('SART+BM3Dsigma0.50',
sart_n_iter=n_iter,
sart_relaxation=0.15,
bm3d_sigma=0.50),
SartBM3DReconstructor('SART+BM3Dsigma0.70',
sart_n_iter=n_iter,
sart_relaxation=0.15,
bm3d_sigma=0.70),
SartBM3DReconstructor('SART+BM3Dsigma0.90',
sart_n_iter=n_iter,
sart_relaxation=0.15,
bm3d_sigma=0.90),
]
imgs = [r.calc(sinogram, theta) for r in recons]
for r in recons:
mse, psnr, ssim = r.eval(gt)
print("{}: MSE:{:.5f} PSNR:{:.5f} SSIM:{:.5f}".format(
r.name, mse, psnr, ssim))
plot_grid([gt] + imgs, FOCUS=FOCUS, save_name='conventional.png')
'DGRH',
dip_n_iter=2501,
net='skipV2',
lr=0.01,
reg_std=1. / 100,
w_proj_loss=0.98,
# w_perceptual_loss=0.01,
w_tv_loss=0.01,
w_ssim_loss=0.01)
img_sart = recon_sart.calc(sinogram, theta)
recon_dip.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log/')
recon_dip2.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log/')
img_dip = recon_dip.calc(sinogram, theta)
img_dip2 = recon_dip2.calc(sinogram, theta)
recons = [recon_sart, recon_dip, recon_dip2]
for r in recons:
mse, psnr, ssim = r.eval(gt)
print("{}:{}: MSE:{:.5f} PSNR:{:.5f} SSIM:{:.5f}".format(
dose, r.name, mse, psnr, ssim))
plot_grid([img_sart, img_dip, img_dip2],
FOCUS=FOCUS,
save_name='dose' + str(dose) + '.png',
dpi=500)
imgs.append(img_dip)
plot_grid([gt] + imgs, FOCUS=FOCUS, save_name='dose.png', dpi=500)
def test(fname, label, n_proj=32, noise_pow=25.0):
dgr_iter = 4000
lr = 0.01
net = 'skip'
noise_std = 1./100
gt, sinogram, theta, FOCUS = image_to_sparse_sinogram(fname,
channel=1, n_proj=n_proj, size=512,
angle1=0.0, angle2=180.0, noise_pow=noise_pow)
logging.warning('Starting')
logging.warning('fname: %s %s',label, fname)
logging.warning('n_proj: %s', n_proj)
logging.warning('noise_pow: %s', noise_pow)
logging.warning('dgr_n_iter: %s', dgr_iter)
logging.warning('dgr_lr: %s', lr)
logging.warning('dgr_net: %s', net)
logging.warning('dgr_noise_std: %s', noise_std)
recons = [
IRadonReconstructor('FBP'),
SartReconstructor('SART', sart_n_iter=40, sart_relaxation=0.15),
SartTVReconstructor('SART+TV',
sart_n_iter=40, sart_relaxation=0.15,
tv_weight=0.5, tv_n_iter=100),
SartBM3DReconstructor('SART+BM3D',
sart_n_iter=40, sart_relaxation=0.15,
bm3d_sigma=0.5),
DgrReconstructor('DIP_1.00_0.00_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=1.0,
w_perceptual_loss=0.0,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.99_0.01_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.99,
w_perceptual_loss=0.01,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.90_0.10_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.90,
w_perceptual_loss=0.10,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.50_0.50_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.5,
w_perceptual_loss=0.5,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.10_0.90_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.10,
w_perceptual_loss=0.90,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.01_0.99_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.01,
w_perceptual_loss=0.99,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.00_1.00_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.0,
w_perceptual_loss=1.0,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.99_0.00_0.01_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.99,
w_perceptual_loss=0.0,
w_tv_loss=0.01
),
DgrReconstructor('DIP_0.90_0.00_0.10_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.9,
w_perceptual_loss=0.0,
w_tv_loss=0.1
),
DgrReconstructor('DIP_0.50_0.00_0.50_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.5,
w_perceptual_loss=0.0,
w_tv_loss=0.5
),
DgrReconstructor('DIP_0.10_0.00_0.90_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.1,
w_perceptual_loss=0.0,
w_tv_loss=0.9
),
DgrReconstructor('DIP_0.01_0.00_0.99_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.01,
w_perceptual_loss=0.0,
w_tv_loss=0.99
),
DgrReconstructor('DIP_0.00_0.00_1.0_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.00,
w_perceptual_loss=0.0,
w_tv_loss=1.0
),
DgrReconstructor('DIP_0.33_0.33_0.33_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.33,
w_perceptual_loss=0.33,
w_tv_loss=0.33
),
DgrReconstructor('DIP_0.8_0.10_0.10_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.8,
w_perceptual_loss=0.1,
w_tv_loss=0.1
),
DgrReconstructor('DIP_0.98_0.01_0.01_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.98,
w_perceptual_loss=0.01,
w_tv_loss=0.01
),
DgrReconstructor('DIP_0.10_0.80_0.10_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.10,
w_perceptual_loss=0.80,
w_tv_loss=0.10
),
DgrReconstructor('DIP_0.01_0.98_0.01_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.01,
w_perceptual_loss=0.98,
w_tv_loss=0.01
),
]
img_sart_bm3d = recons[3].calc(sinogram, theta)
imgs = []
for recon in recons:
if type(recon) == DgrReconstructor:
recon.set_for_metric(gt, img_sart_bm3d, FOCUS=FOCUS, log_dir='../log/dip')
imgs.append(recon.calc(sinogram))
mse, psnr, ssim = recon.eval(gt)
recon.save_result()
logstr = "{}: MSE:{:.5f} PSNR:{:.5f} SSIM:{:.5f}".format(
recon.name, mse, psnr, ssim
)
logging.info(logstr)
plot_grid([gt] + imgs,
FOCUS=FOCUS, save_name=label+'.png', dpi=500)
logging.warning('Done. Results saved as %s', label+'.png')
recon_sart,
recon_sart_tv,
recon_bm3d,
recon_dip,
recon_dip_rand,
]
# recon_n2self_selfsuper,
# recon_n2self_learned_single,
# recon_n2self_learned_selfsuper]
for r in recons:
mse, psnr, ssim = r.eval(gt)
print("{}: MSE:{:.5f} PSNR:{:.5f} SSIM:{:.5f}".format(
r.name, mse, psnr, ssim))
plot_grid(
[
gt,
img_fbp,
img_sart,
img_sart_tv,
img_sart_bm3d,
img_dip,
img_dip_rand,
],
#img_n2self_selfsuper,
#img_n2self_learned_single, img_n2self_learned_selfsuper],
FOCUS=FOCUS,
save_name='all.png',
dpi=500)
'DGRH',
dip_n_iter=2501,
net='skipV2',
lr=0.01,
reg_std=1. / 100,
w_proj_loss=0.98,
# w_perceptual_loss=0.01,
w_tv_loss=0.01,
w_ssim_loss=0.01)
img_sart = recon_sart.calc(sinogram, theta)
recon_dip.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log/')
recon_dip2.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log/')
img_dip = recon_dip.calc(sinogram, theta)
img_dip2 = recon_dip2.calc(sinogram, theta)
recons = [recon_sart, recon_dip, recon_dip2]
for r in recons:
mse, psnr, ssim = r.eval(gt)
print("{}:{}: MSE:{:.5f} PSNR:{:.5f} SSIM:{:.5f}".format(
view, r.name, mse, psnr, ssim))
plot_grid([img_sart, img_dip, img_dip2],
FOCUS=FOCUS,
save_name='view' + str(view) + '.png',
dpi=500)
imgs.append(img_dip)
plot_grid([gt] + imgs, FOCUS=FOCUS, save_name='view.png', dpi=500)
w_ssim_loss=0.01,
channels=[64, 128, 256],
)
img_sart = recon_sart.calc(sinogram, theta)
img_sart_tv = recon_sart_tv.calc(sinogram, theta)
recon_dip1.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log/')
img_dip1 = recon_dip1.calc(sinogram, theta)
recon_dip2.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log/')
img_dip2 = recon_dip2.calc(sinogram, theta)
recon_dip3.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log/')
img_dip3 = recon_dip3.calc(sinogram, theta)
recons = [
recon_sart,
recon_sart_tv,
recon_dip1,
recon_dip2,
recon_dip3,
]
for r in recons:
mse, psnr, ssim = r.eval(gt)
print("{}: MSE:{:.5f} PSNR:{:.5f} SSIM:{:.5f}".format(
r.name, mse, psnr, ssim))
plot_grid([gt, img_sart, img_sart_tv, img_dip1, img_dip2, img_dip3],
FOCUS=FOCUS,
save_name='net_arc.png',
dpi=500)
def calc(self, projs, theta):
# recon params
self.N_PROJ = len(theta)
self.theta = torch.from_numpy(theta).to(self.DEVICE)
self.radon = Radon(self.IMAGE_SIZE, self.theta, True).to(self.DEVICE)
self.iradon = IRadon(self.IMAGE_SIZE, self.theta, True).to(self.DEVICE)
# start recon
if not os.path.exists(self.log_dir):
os.mkdir(self.log_dir)
net = self._get_net()
x_initial = np_to_torch(self.noisy).type(self.DTYPE)
img_gt_torch = np_to_torch(self.gt).type(self.DTYPE)
net_input = torch.rand(1, self.INPUT_DEPTH,
self.IMAGE_SIZE, self.IMAGE_SIZE).type(self.DTYPE)
net_input_saved = net_input.detach().clone()
noise = net_input.detach().clone()
# Compute number of parameters
s = sum([np.prod(list(p.size())) for p in net.parameters()]);
print ('Number of params: %d' % s)
# Optimizer
optimizer = torch.optim.Adam(net.parameters(), lr=self.lr)
cur_lr = self.lr
projs = np_to_torch(projs).type(self.DTYPE)#self.radon(img_gt_torch).detach().clone()
#np_to_torch(projs).type(self.DTYPE) #
# Iterations
loss_hist = []
rmse_hist = []
ssim_hist = []
ssim_noisy_hist = []
psnr_hist = []
psnr_noisy_hist = []
best_network = None
best_result = None
print('Reconstructing with DIP...')
for i in tqdm(range(self.n_iter)):
# iter
optimizer.zero_grad()
if self.reg_std > 0:
net_input = net_input_saved + (noise.normal_() * self.reg_std)
x_iter = net(net_input)
loss, (proj_l, percep_l, tv_l, ssim_l) = self._calc_loss(x_iter, projs, x_initial)
loss.backward()
optimizer.step()
# metric
if i % self.SHOW_EVERY == 0:
x_iter_npy = np.clip(torch_to_np(x_iter), 0, 1).astype(np.float64)
rmse_hist.append(
mean_squared_error(x_iter_npy, self.gt))
ssim_hist.append(
structural_similarity(x_iter_npy, self.gt, multichannel=False)
)
ssim_noisy_hist.append(
structural_similarity(x_iter_npy, self.noisy, multichannel=False)
)
psnr_hist.append(
peak_signal_noise_ratio(x_iter_npy, self.gt)
)
psnr_noisy_hist.append(
peak_signal_noise_ratio(x_iter_npy, self.noisy)
)
loss_hist.append(loss.item())
print('{}/{}- psnr: {:.3f} - psnr_noisy: {:.3f} - ssim: {:.3f} - ssim_noisy: {:.3f} - rmse: {:.5f} - loss: {:.5f} '.format(
self.name, i, psnr_hist[-1], psnr_noisy_hist[-1], ssim_hist[-1], ssim_noisy_hist[-1], rmse_hist[-1], loss_hist[-1]
))
#print( proj_l.item(), ssim_l.item())
# if psnr_noisy_hist[-1] / max(psnr_noisy_hist) < 0.92:
# print('Falling back to previous checkpoint.')
# for g in optimizer.param_groups:
# g['lr'] = cur_lr / 10.0
# cur_lr = cur_lr / 10.0
# print("optimizer.lr", cur_lr)
# # load network
# for new_param, net_param in zip(best_network, net.parameters()):
# net_param.data.copy_(new_param.cuda())
if i > 2:
if loss_hist[-1] < min(loss_hist[0:-1]):
# save network
best_network = [x.detach().cpu() for x in net.parameters()]
best_result = x_iter_npy.copy()
plot_grid([x_iter_npy], self.FOCUS, save_name=self.log_dir+'/{}.png'.format(i))
self.image_r = best_result
return self.image_r
# w_perceptual_loss=0.01,
w_tv_loss=0.01,
w_ssim_loss=0.01)
img_sart = recon_sart.calc(sinogram, theta)
img_sart_tv = recon_sart_tv.calc(sinogram, theta)
recon_dip1.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log/')
img_dip1 = recon_dip1.calc(sinogram, theta)
recon_dip2.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log/')
img_dip2 = recon_dip2.calc(sinogram, theta)
recon_dip3.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='log/')
img_dip3 = recon_dip3.calc(sinogram, theta)
recons = [
recon_sart,
recon_sart_tv,
recon_dip1,
recon_dip2,
recon_dip3,
]
for r in recons:
mse, psnr, ssim = r.eval(gt)
print("{}: MSE:{:.5f} PSNR:{:.5f} SSIM:{:.5f}".format(
r.name, mse, psnr, ssim))
plot_grid([gt, img_sart, img_sart_tv, img_dip1, img_dip2, img_dip3],
FOCUS=FOCUS,
save_name='num_params.png',
dpi=500)
def _calc_unsupervised(self, projs, theta):
if not os.path.exists(self.log_dir):
os.mkdir(self.log_dir)
projs = np_to_torch(projs).type(self.DTYPE)
#norm = transforms.Normalize(projs_torch[0].mean((1,2)), projs_torch[0].std((1,2)))
for i in tqdm(range(self.n_iter)):
# iter
self.optimizer.zero_grad()
# train
if i % self.SHOW_EVERY != 0:
self.net.train()
self.filter.train()
net_input, mask = self.masker.mask( projs, self.i_iter % (self.masker.n_masks - 1) )
x_iter = self.net(
self.ir(net_input)
)
loss = self.mse(
self.r(x_iter)*mask,
projs*mask
)
# val
else:
self.net.eval()
self.filter.eval()
x_iter = self.net(
self.ir(projs)
)
loss = self.mse(
self.r(x_iter),
projs
)
loss.backward()
self.optimizer.step()
# metric
if i % self.SHOW_EVERY == 0:
x_iter_npy = np.clip(torch_to_np(x_iter), 0, 1).astype(np.float64)
self.rmse_hist.append(
mean_squared_error(x_iter_npy, self.gt))
self.ssim_hist.append(
structural_similarity(x_iter_npy, self.gt, multichannel=False)
)
self.psnr_hist.append(
peak_signal_noise_ratio(x_iter_npy, self.gt)
)
self.loss_hist.append(loss.item())
print('{}/{}- psnr: {:.3f} - ssim: {:.3f} - rmse: {:.5f} - loss: {:.5f} '.format(
self.name, i, self.psnr_hist[-1], self.ssim_hist[-1], self.rmse_hist[-1], self.loss_hist[-1]
))
if i > 2:
if self.loss_hist[-1] < min(self.loss_hist[0:-1]):
# save network
# best_network = [x.detach().cpu() for x in net.parameters()]
self.best_result = x_iter_npy.copy()
else:
self.best_result = x_iter_npy.copy()
plot_grid([self.gt, x_iter_npy], self.FOCUS, save_name=self.log_dir+'/{}.png'.format(i))
self.image_r = self.best_result
return self.image_r
