ColorUNet: A convolutional classification approach to colorization

Abstract

This paper tackles the challenge of colorizing grayscale images. We take a deep convolutional neural network approach, and choose to take the angle of classification, working on a finite set of possible colors. Similarly to a recent paper, we implement a loss and a prediction function that favor realistic, colorful images rather than "true" ones.We show that a rather lightweight architecture inspired by the U-Net, and trained on a reasonable amount of pictures of landscapes, achieves satisfactory results on this specific subset of pictures. We show that data augmentation significantly improves the performance and robustness of the model, and provide visual analysis of the prediction confidence.We show an application of our model, extending the task to video colorization. We suggest a way to smooth color predictions across frames, without the need to train a recurrent network designed for sequential inputs.

Single Energy X-ray Image Colorization Using Convolutional Neural Network for Material Discrimination

“…The proposed CNN architecture is an encoder-decoder architecture inspired by the architecture of UNet [29], which initially performed semantic segmentation and is common in colorization research, as mentioned in [30][31][32][33]. U-Net architecture is employed to perform the task of single-energy X-ray image colorization; however, the original study of UNet [29] has a different objective: image segmentation.…”