PoissonLikelihood#

class deepinv.optim.PoissonLikelihood(gain=1.0, bkg=0, denormalize=True)[source]#

Poisson negative log-likelihood.

\[\datafid{z}{y} = -y^{\top} \log(z+\beta)+1^{\top}z\]

where \(y\) are the measurements, \(z\) is the estimated (positive) density and \(\beta\geq 0\) is an optional background level.

Note

The function is not Lipschitz smooth w.r.t. \(z\) in the absence of background (\(\beta=0\)).

Parameters:

gain (float) – gain of the measurement \(y\). Default: 1.0.
bkg (float) – background level \(\beta\). Default: 0.
denormalize (bool) – if True, the measurement is multiplied by the gain. Default: True.

Examples using `PoissonLikelihood`:#