Colorful Image Colorization

Richard Zhang, Phillip Isola, Alexei A. Efros, 2016

Given  a  grayscale  photograph  as  input,  this  paper  attacks the problem of hallucinating a plausible colour version of the photograph.This problem is clearly under constrained, so previous approaches have either relied on significant user interaction or resulted in desaturated colorisation. We propose a fully automatic approach that produces vibrant and realistic colorisation. We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colours in the result. The system is implemented as a feed-forward pass in a CNN at test time and is trained on over a million colour images.

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