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We present a novel technique to automatically colorize grayscale images thatcombine the U-Net model and Fusion Layer features. This approach allows themodel to learn the colorization of images from pre-trained U-Net. Moreover, theFusion layer is applied to merge local information results dependent on smallimage patches with global priors of an entire image on each class, formingvisually more compelling colorization results. Finally, we validate ourapproach with a user study evaluation and compare it against state-of-the-art,resulting in improvements.

Image Colorization using U-Net with Skip Connections and Fusion Layer on Landscape Images