摘要
传统的单通道盲去卷积的方法存在仅能从混合信号中分离出2路源信号的局限,考虑到以上问题,该文提出一种基于优化的深度卷积生成对抗网络的单通道盲去卷积算法(SCBDC),能从1路混合信号中分离和解卷积出3路以上的独立源信号和混合矩阵。该文实验在汉字和遮挡图像数据集上进行,随机选择4路信号与混合矩阵进行卷积混合,实验结合峰值信噪比(PSNR)和信号相关性指标来评价分离的效果,结果显示,该算法能够有效地分离出多路源信号并去卷积。
Traditional single-channel blind deconvolution method has the limitation that it can only separate two sources from a mixture.Considering this problem,a Single-Channel Blind Deconvolution algorithm based on optimized deep Convolutional generative adversarial networks(SCBDC)is proposed to separate and deconvolve more than three independent sources and mixing matrix only from a mixture.The experiments are carried on the occlusion Chinese character image datasets,four sources are randomly selected to be mixed with mixing matrix.Peak Signal to Noise Ratio(PSNR)and signal correlation index are combined to evaluate the separation effect.The result shows that the multiple sources can be effectively separated and deconvolved.
作者
刘婷
尹甜甜
龚真颖
郭一娜
LIU Ting;YIN Tiantian;GONG Zhenying;GUO Yina(Electronics and Communication Engineering,Taiyuan University of Science and Technology,Taiyuan 030024,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2022年第1期230-236,共7页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61301250)
国家留学基金委地区合作与高层次人才培养项目[2020]1417
山西省重点研发计划资助项目(201803D421035)
山西省自然科学优秀青年基金(201901D211313)
山西省回国留学人员科研教研资助项目(HGKY2019080)。
关键词
盲源分离
单通道盲去卷积
多源信号分离
生成对抗网络
混合矩阵估计
Blind source seperation
Single-channel blind deconvolution
Multi-source separation
Generative adversarial networks
Mixing matrix estimation
