  We propose a new method that uses deep learning techniques to solve the
inverse problems. The inverse problem is cast in the form of learning an
end-to-end mapping from observed data to the ground-truth. Inspired by the
splitting strategy widely used in regularized iterative algorithm to tackle
inverse problems, the mapping is decomposed into two networks, with one
handling the inversion of the physical forward model associated with the data
term and one handling the denoising of the output from the former network,
i.e., the inverted version, associated with the prior/regularization term. The
two networks are trained jointly to learn the end-to-end mapping, getting rid
of a two-step training. The training is annealing as the intermediate variable
between these two networks bridges the gap between the input (the degraded
version of output) and output and progressively approaches to the ground-truth.
The proposed network, referred to as InverseNet, is flexible in the sense that
most of the existing end-to-end network structure can be leveraged in the first
network and most of the existing denoising network structure can be used in the
second one. Extensive experiments on both synthetic data and real datasets on
the tasks, motion deblurring, super-resolution, and colorization, demonstrate
the efficiency and accuracy of the proposed method compared with other image
processing algorithms.
