def SQSOneStep(reconNet,
x,
z,
ref,
prj,
weight,
normImg,
projectorNorm,
args,
verbose=0):
# projection term
if not reconNet.rotview % args.nSubsets == 0:
raise ValueError('reconNet.rotview cannot be divided by args.nSubsets')
inds = helper.OrderedSubsetsBitReverse(reconNet.rotview, args.nSubsets)
angles = np.array([reconNet.angles[i] for i in inds], np.float32)
prj = prj[:, :, inds, :]
weight = weight[:, :, inds, :]
nAnglesPerSubset = int(reconNet.rotview / args.nSubsets)
x_new = np.copy(z)
for i in range(args.nSubsets):
if verbose:
print('set%d' % i, end=',', flush=True)
curAngles = angles[i * nAnglesPerSubset:(i + 1) * nAnglesPerSubset]
curWeight = weight[:, :,
i * nAnglesPerSubset:(i + 1) * nAnglesPerSubset, :]
fp = reconNet.cDDFanProjection3d(x_new, curAngles) / projectorNorm
dprj = fp - prj[:, :, i * nAnglesPerSubset:
(i + 1) * nAnglesPerSubset, :] / projectorNorm
bp = reconNet.cDDFanBackprojection3d(curWeight * dprj,
curAngles) / projectorNorm
# tvs1, tvs2, _ = PriorFunctionSolver.cTVSQS2D(x_new, args.eps)
sqsNlm = 4 * (
x_new - HYPR_NLM.NLM(x_new, ref, args.searchSize, args.kernelSize,
args.kernelStd, args.sigma))
x_new = x_new - (args.nSubsets * bp + args.betaRecon * sqsNlm) / (
normImg + args.betaRecon * 8)
z = x_new + args.nesterov * (x_new - x)
x = np.copy(x_new)
# get loss function
fp = reconNet.cDDFanProjection3d(x, angles) / projectorNorm
dataLoss = 0.5 * np.sum(weight * (fp - prj / projectorNorm)**2)
# nlm loss
nlm = HYPR_NLM.NLM(x, ref, args.searchSize, args.kernelSize,
args.kernelStd, args.sigma)
nlm2 = HYPR_NLM.NLM(x**2, ref, args.searchSize, args.kernelSize,
args.kernelStd, args.sigma)
nlmLoss = np.sum(x**2 - 2 * x * nlm + nlm2)
return x, z, dataLoss, nlmLoss
# In[42]:
weights = np.sqrt(np.exp(-prjs * args.imgNorm))
projectorNorm = CalcProjectorNorm(reconNet, weights)
normImg = reconNet.cDDFanNormImg3d(prjs, weights) / projectorNorm / projectorNorm
# masks = np.ones_like(masks)
# In[43]:
x = imgs
guide = np.ones_like(x)
xd = HYPR_NLM.NLM(x, guide, [1,3,3], [1,1,1], 1, 1)
if showPlots:
plt.figure(figsize=[12,6])
plt.subplot(121); plt.imshow(x[0, 128:-128, 128:-128, 0].T, 'gray', vmin=args.vmin, vmax=args.vmax)
plt.subplot(122); plt.imshow((x - refs)[0, 128:-128, 128:-128, 0].T, 'gray', vmin=-0.1, vmax=0.1)
rmse0_roi = np.sqrt(np.mean((x - refs)[0, 128:-128, 128:-128, 0]**2))
print (rmse0_roi)
# In[44]:
def SQSOneStep(reconNet, x, z, ref, prj, weight, normImg, projectorNorm, args, verbose = 0):
# projection term
if not reconNet.rotview % args.nSubsets == 0:
return projectorNorm
# In[13]:
weights = np.sqrt(np.exp(-prjs * args.imgNorm))
projectorNorm = CalcProjectorNorm(reconNet, weights)
normImg = reconNet.cDDFanNormImg3d(prjs,
weights) / projectorNorm / projectorNorm
# masks = np.ones_like(masks)
# In[14]:
x = imgs
x0 = np.copy(x)
xd = HYPR_NLM.NLM(x, x0, args.searchSize, args.kernelSize, args.kernelStd,
args.sigma)
if showPlots:
plt.figure(figsize=[12, 6])
plt.subplot(121)
plt.imshow(x0[0, 128:-128, 128:-128, 0].T,
'gray',
vmin=args.vmin,
vmax=args.vmax)
plt.subplot(122)
plt.imshow(xd[0, 128:-128, 128:-128, 0].T,
'gray',
vmin=args.vmin,
vmax=args.vmax)
rmse0_roi = np.sqrt(np.mean((x0 - refs)[0, 128:-128, 128:-128, 0]**2))
print(rmse0_roi)