Advanced multi-gans towards near to real image and video colorization

Multi-GANs, inspired by traditional GAN, divide each problem space into several smaller and more homogeneous subspaces. It is an architecture of multiple generative adversarial networks that work together to achieve the highest output quality. This paper presents Advanced Multi-GANs architecture for colorization based on two novelties, including the cluster numbers and the color harmonies. Advanced Multi-GANs can intelligently decide the number of clusters using the input test image and its scene complexity, leading to much more realistic colorization. Also, color harmony, which defines a rational relation between pixels of frames and their generated colors, is proposed to keep the harmony of the colors among a sequence of frames in video colorizing. Color harmony helps avoid changing the colors of the same objects between video frames. In experimental results, the evaluation of this study with several protocols, including image and video colorization, is provided. In addition to visual qualitative evaluation, the performance of the proposed method is quantitatively measured in the Advanced Multi-GAN framework. The experimental results show much more realistic outputs in comparison to the traditional approaches and state-of-the-art.