GRAY-SCALE IMAGE COLORIZATION USING CYCLE-CONSISTENT GENERATIVE ADVERSARIAL NETWORKS WITH RESIDUAL STRUCTURE ENHANCER

The colorization of gray-scale images has always been a challenging task in computer vision. Recently, novel approaches have been introduced for unsupervised image translation between two domains using Generative Adversarial Networks (GANs). Since one can consider the gray-scale and colorful images as two separate domains, we propose a two-stage cycle-consistent network architecture to produce convincible images. First, an intermediate image is generated with a relatively uncomplicated objective function at the output. Next, at the second stage, the intermediate image is enhanced via a residual network structure with a more complicated objective function. Furthermore, by employing two inverse networks, a cycle-consistent architecture is formed at both stages. The proposed model is trained on the ImageNet dataset, and the achieved outcomes demonstrate exceptional performance comparing with the state-of-the-art models.