摘要
针对单通道极端欠定状况及传统盲源分离(blind source separation,BSS)方法难以克服源信号独立、非高斯分布等多项约束和先验知识缺乏等问题,基于生成对抗模型的网络架构,提出了一种基于注意机制的单通道盲图像分离方法,将基于流形排序的视觉注意机制嵌入到分离网络中,增强目标的关键信息,以生成思想迭代分离混合图像。实验结果表明,融合注意机制的生成对抗网络能仅从单源数据中学习,无需多种先验约束,比经典盲源分离方法有更高的分离精度,与已知分布的神经网络分离方法相比,能更有效地分离单通道混合图像。
Traditional blind source separation(BSS)algorithms have multiple constraints such as independent source signals,non-Gaussian distribution,and lack of prior knowledge.Therefore,a single-channel blind image separation algorithm was proposed based on attention mechanism GAN in this work,and it would have more hopeful prospects in blind image separation task.The algorithm embedded the visual attention mechanism based on manifold sorting into the separation network to enhance the key information of the target.The algorithm extracted the features,strengthened the edge guidance,and separated the mixed images with the generative idea.Experimental results show that the adversarial network incorporating attention mechanism learns from a single source of data,it is significantly better than the classic blind source separation algorithm and the neural network separation algorithm with known distribution.
作者
徐金东
孙潇
马咏莉
欧世峰
XU Jindong;SUN Xiao;MA Yongli;OU Shifeng(School of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, China;School of Opto-Electronic Information Science and Technology, Yantai University, Yantai, Shandong 264005, China)
出处
《中国科技论文》
CAS
北大核心
2021年第3期266-270,共5页
China Sciencepaper
基金
山东省自然科学基金资助项目(ZR2019MF060,ZR2017MF008)
山东省高等学校科研计划重点项目(J18KZ016)
烟台科技计划项目(2018YT06000271)。
关键词
盲图像分离
单通道
生成对抗网络
注意机制
生成模型
blind image separation
single channel
generative adversarial network
attention mechanism
generative model