spyrit.core.recon.DCNet.reconstruct

DCNet.reconstruct(x)

Reconstruction step of a reconstruction network

Args:

x: raw measurement vectors

Shape:

x: raw measurement vectors with shape \((BC,2M)\)

output: reconstructed images with shape \((BC,1,H,W)\)

Example:

>>> B, C, H, M = 10, 1, 64, 64**2
>>> Ord = torch.ones((H,H))
>>> meas = HadamSplit(M, H, Ord)
>>> noise = NoNoise(meas)
>>> prep = SplitPoisson(1.0, M, H*H)
>>> sigma = torch.rand([H**2, H**2])
>>> recnet = DCNet(noise,prep,sigma)
>>> x = torch.rand((B*C,2*M), dtype=torch.float)
>>> z = recnet.reconstruct(x)
>>> print(z.shape)
torch.Size([10, 1, 64, 64])