  Building on crucial insights into the determining factors of the visual
integrity of an image and the property of deep convolutional neural network
(CNN), we have developed the Deep Feature Consistent Deep Image Transformation
(DFC-DIT) framework which unifies challenging one-to-many mapping image
processing problems such as image downscaling, decolorization (colour to
grayscale conversion) and high dynamic range (HDR) image tone mapping. We train
one CNN as a non-linear mapper to transform an input image to an output image
following what we term the deep feature consistency principle which is enforced
through another pretrained and fixed deep CNN. This is the first work that uses
deep learning to solve and unify these three common image processing tasks. We
present experimental results to demonstrate the effectiveness of the DFC-DIT
technique and its state of the art performances.
