  Recovering the image of an object from its phaseless speckle pattern is
difficult. Let alone the transmission matrix is unknown in multiple scattering
media imaging. Double phase retrieval is a recently proposed efficient method
which recovers the unknown object from its phaseless measurements by two steps
with phase retrieval.
  In this paper, we combine the two steps in double phase retrieval and
construct an end-to-end neural network called TCNN(Transforming Convolutional
Neural Network) which directly learns the relationship between the phaseless
measurements and the object. TCNN contains a special layer called transform
layer which aims to be a bridge between different transform domains. Tested by
the empirical data provided in\cite{Metzler2017Coherent}, images can be
recovered by TCNN with comparable quality compared with state-of-the-art
methods. Not only the transmission matrix needn't to be calculated but also the
time to recover the object can be hugely reduced once the parameters of TCNN
are stable.
