Robust Image Colorization using Self Attention based Progressive Generative Adversarial Network

Automatic image colorization is a very interesting computer graphics problem wherein an input grayscale image is transformed into its RGB domain. However, it is an illposed problem as there can be multiple RGB outcomes for a particular grayscale pixel. The problem further complicates if noise is present in the grayscale image. In this paper, we propose a Robust Image Colorization using Self-attention based Progressive Generative Adversarial Network (RIC-SPGAN) which consists of residual encoder-decoder (RED) network and a Self-attention based progressive Generative network (SP-GAN) in a cascaded form to perform the denoising and colorization of the image. We have used self-attention based progressive network to model the longrange dependencies and gradually enhanced the resolution of the colorized image for faster, stable and variation rich features for generation of the image. We also presented the stabilization technique of the presented generative model. Our model has shown exceptional perceptual results on noisy and normal grayscale images. We have trained our model on ILSVRC2012 [6]. The visual results on images of DIV2K [1] with and without noise has been presented in the paper along with the failure cases of the model.