摘要
自动上色可以将灰度图像转换为色彩合理的自然彩色图像,可以为老旧照片、黑白影视作品等重新恢复颜色,因此在计算机视觉和图形学领域受到广泛关注。然而,为灰度图像分配色彩是一项极具挑战性的任务,存在颜色溢出问题。为解决该问题,提出了一种极化自注意力约束颜色溢出的图像自动上色方法。首先,将前景中的实例和背景分开,降低背景对前景的上色影响,从而减少前景和背景之间的颜色溢出;然后,使用极化自注意力模块把特征分为颜色通道和空间位置两部分,使上色更加准确、具体,从而减少全局图像、实例对象内的颜色溢出;最后,结合融合模块,将全局特征和实例特征通过不同权重融合为一体,完成最终上色。实验结果表明,与ChromaGAN,MemoGAN等算法相比,所提方法在主要指标FID,LPIPS上分别提升了9.7%和10.9%,且SSIM和PSNR指标均达到最优。
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.
作者
刘航
普园媛
吕大华
赵征鹏
徐丹
钱文华
LIU Hang;PU Yuanyuan;LYU Dahua;ZHAO Zhengpeng;XU Dan;QIAN Wenhua(School of Information Science and Engineering,Yunnan University,Kunming 650504,China;University Key Laboratory of Internet of Things Technology and Application,Yunnan Province,Kunming 650504,China)
出处
《计算机科学》
CSCD
北大核心
2023年第3期208-215,共8页
Computer Science
基金
国家自然科学基金(62162068,61271361,61761046,62061049)
云南省应用基础研究面上项目(2018FB100)
云南省科技厅应用基础研究计划重点项目(202001BB050043,2019FA044)。
关键词
图像上色
深度学习
目标检测
自注意力
颜色溢出
Image coloring
Deep learning
Target detection
Self attention
Color overflow
