  Most image deblurring methods assume an over-simplistic image formation model
and as a result are sensitive to more realistic image degradations. We propose
a novel variational framework, that explicitly handles pixel saturation, noise,
quantization, as well as non-linear camera response function due to e.g., gamma
correction. We show that accurately modeling a more realistic image acquisition
pipeline leads to significant improvements, both in terms of image quality and
PSNR. Furthermore, we show that incorporating the non-linear response in both
the data and the regularization terms of the proposed energy leads to a more
detailed restoration than a naive inversion of the non-linear curve. The
minimization of the proposed energy is performed using stochastic optimization.
A dataset consisting of realistically degraded images is created in order to
evaluate the method.
