Polarized Self-attention Constrains Color Overflow in Automatic Coloring of Image

Abstract

Auto coloring transforms grayscale images into reasonable colored versions of natural color images,allowing the restoration of color for old photographs,black-and-white films,videos,etc.Therefore,it is widely concerned in the realms of computer vision and graphics.Nevertheless,allocating colors to grayscale images is a highly challenging mission with a color overflow pro-blem.To address the problem,a technique for automatic coloring of images with polarized self-attention constrained color overflow is proposed.At first,separating instances in the foreground from the background minimizes the coloring effect of the background against the foreground,to mitigate the color overflow among the foreground and background.Second,the polarized self-attention block splits the features into color channels and spatial locations for more accurate and specific coloring,which reduces the color overflow within the global image,instance objects.At last,the fusion module is combined to integrate the global features and instance features through different weights to accomplish the ultimate coloring.Experiment results show that the main indexes FID and LPIPS are improved by 9.7% and 10.9% respectively,and the indexes SSIM and PSNR are optimal compared with ChromaGAN and MemoGAN.

Keywords

• image coloring|deep learning|target detection|self attention|color overflow