“Anime Sketch Coloring With Swish-Gated Residual U-Net and Spectrally Normalized GAN (SSN-GAN)”, 2019-08-12 (; similar):
Anime sketch coloring is to fill various colors into the black-and-white anime sketches and finally obtain the color anime images. Recently, anime sketch coloring has become a new research hotspot in the field of deep learning. In anime sketch coloring, generative adversarial networks (GANs) have been used to design appropriate coloring methods and achieved some results. However, the existing methods based on GANs generally have low-quality coloring effects, such as unreasonable color mixing, poor color gradient effect.
In this paper, an efficient anime sketch coloring method using swish-gated residual U-Net (SGRU) and spectrally normalized GAN (SNGAN) has been proposed to solve the above problems.
The proposed method is called spectrally normalized GAN with swish-gated residual U-Net (SSN-GAN). In SSN-GAN, SGRU is used as the generator. SGRU is the U-Net with the proposed swish layer and swish-gated residual blocks (SGBs). In SGRU, the proposed swish layer and swish-gated residual blocks (SGBs) effectively filter the information transmitted by each level and improve the performance of the network. The perceptual loss and the per-pixel loss are used to constitute the final loss of SGRU. The discriminator of SSN-GAN uses spectral normalization as a stabilizer of training of GAN, and it is also used as the perceptual network for calculating the perceptual loss. SSN-GAN can automatically color the sketch without providing any coloring hints in advance and can be easily end-to-end trained.
Experimental results show that our method performs better than other state-of-the-art coloring methods, and can obtain colorful anime images with higher visual quality.