  A CNN-based interactive contrast enhancement algorithm, called IceNet, is
proposed in this work, which enables a user to adjust image contrast easily
according to his or her preference. Specifically, a user provides a parameter
for controlling the global brightness and two types of scribbles to darken or
brighten local regions in an image. Then, given these annotations, IceNet
estimates a gamma map for the pixel-wise gamma correction. Finally, through
color restoration, an enhanced image is obtained. The user may provide
annotations iteratively to obtain a satisfactory image. IceNet is also capable
of producing a personalized enhanced image automatically, which can serve as a
basis for further adjustment if so desired. Moreover, to train IceNet
effectively and reliably, we propose three differentiable losses. Extensive
experiments show that IceNet can provide users with satisfactorily enhanced
images.
