Perceptual Compressive Sensing

摘要：

　　Compressive sensing(CS)works to acquire measurements at sub-Nyquist rate and recover the scene images.Existing CS methods always recover the scene images in pixel level.This causes the smoothness of recovered images and lack of structure information,especially at a low measurement rate.To overcome this drawback,in this paper,we propose perceptual CS to obtain high-level structured recovery.Our task no longer focuses on pixel level.Instead,we work to make a better visual effect.In detail,we employ perceptual loss,defined on feature level,to enhance the structure information of the recovered images.Experiments show that our method achieves better visual results with stronger structure information than existing CS methods at the same measurement rate.

关键词： Compressive sensing Perceptual loss Fully convolutional network Low-level computer vision Semantic reconstruction

作者: Jiang Du Xuemei Xie Chenye Wang Guangming Shi

作者单位: School of Artificial Intelligence,Xidian University,Xian 710071,China

会议类型: 国际会议

会议名称: 中国模式识别与计算机视觉大会(PRCV2018)

会议地点: 广州

会议语种:英文

页码: 268-279

在线出版日期: 2018-11-23（万方平台首次上网日期，不代表论文的发表时间）