  Colorization methods using deep neural networks have become a recent trend.
However, most of them do not allow user inputs, or only allow limited user
inputs (only global inputs or only local inputs), to control the output
colorful images. The possible reason is that it's difficult to differentiate
the influence of different kind of user inputs in network training. To solve
this problem, we present a novel deep colorization method, which allows
simultaneous global and local inputs to better control the output colorized
images. The key step is to design an appropriate loss function that can
differentiate the influence of input data, global inputs and local inputs. With
this design, our method accepts no inputs, or global inputs, or local inputs,
or both global and local inputs, which is not supported in previous deep
colorization methods. In addition, we propose a global color theme
recommendation system to help users determine global inputs. Experimental
results shows that our methods can better control the colorized images and
generate state-of-art results.
