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
def SQSOneStep(reconNet,
x,
x_nesterov,
z,
masks,
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(x_nesterov)
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
x_new = x_new - (args.nSubsets * bp + args.gamma * args.betaRecon *
(x_new - z) * masks) / (
normImg + args.gamma * args.betaRecon * masks)
x_nesterov = x_new + args.nesterov * (x_new - x)
x = np.copy(x_new)
# get loss function
fp = reconNet.cDDFanProjection3d(x, angles) / projectorNorm
dataLoss = np.sum(weight * (fp - prj / projectorNorm)**2)
return x, x_nesterov, dataLoss
