Abstract
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.
Abstract (translated)
压缩感知（CS）用于以亚奈奎斯特速率获取测量值并恢复场景图像。现有的CS方法总是以像素级恢复场景图像。这导致恢复图像的平滑性和缺乏结构信息，尤其是在低测量速率下。为了克服这个缺点，在本文中，我们提出感知CS来获得高级结构化恢复。我们的任务不再关注像素级别。相反，我们努力创造更好的视觉效果。详细地，我们采用在特征级别上定义的感知损失来增强恢复图像的结构信息。实验表明，在相同的测量速率下，我们的方法可以获得比现有CS方法更强的结构信息的更好的视觉效果。
URL
https://arxiv.org/abs/1802.00176
