dip_n_iter=dgr_iter,
net='skip',
lr=0.01,
reg_std=1./100,
w_proj_loss=0.98,
# w_perceptual_loss=0.1,
w_tv_loss=0.01,
w_ssim_loss=0.01
)
img_fbp = recon_fbp.calc(sinogram, theta)
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_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
))
sart_n_iter=40,
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)
# n2self_selfsupervised=False,
# net='skip', lr=0.01, )
# recon_n2self_learned_selfsuper = N2SelfReconstructor('N2Self_Learned_SelfSupervised',
# n2self_n_iter=4001, n2self_proj_ratio=0.2,
# n2self_weights='training-07/iter_132000.pth',
# n2self_selfsupervised=True,
# net='skip', lr=0.01, )
img_fbp = recon_fbp.calc(sinogram, theta)
img_sart = recon_sart.calc(sinogram, theta)
img_sart_tv = recon_sart_tv.calc(sinogram, theta)
img_sart_bm3d = recon_bm3d.calc(sinogram, theta)
recon_dip.set_for_metric(gt, img_sart, FOCUS=FOCUS, log_dir='../log/dip')
img_dip = recon_dip.calc(sinogram, theta)
recon_dip_rand.set_for_metric(gt,
img_sart,
FOCUS=FOCUS,
log_dir='../log/dip')
img_dip_rand = recon_dip_rand.calc(sinogram, theta)
# recon_n2self_selfsuper.set_for_metric(gt, img_sart_tv, FOCUS=FOCUS, log_dir='../log/dip')
# img_n2self_selfsuper = recon_n2self_selfsuper.calc(sinogram, theta)
# recon_n2self_learned_single.set_for_metric(gt, img_sart_tv, FOCUS=FOCUS, log_dir='../log/dip')
# img_n2self_learned_single = recon_n2self_learned_single.calc(sinogram, theta)
# recon_n2self_learned_selfsuper.set_for_metric(gt, img_sart_tv, FOCUS=FOCUS, log_dir='../log/dip')
# img_n2self_learned_selfsuper = recon_n2self_learned_selfsuper.calc(sinogram, theta)