Perceptual Compressive Sensing
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.
Cite
@article{arxiv.1802.00176,
title = {Perceptual Compressive Sensing},
author = {Jiang Du and Xuemei Xie and Chenye Wang and Guangming Shi},
journal= {arXiv preprint arXiv:1802.00176},
year = {2018}
}
Comments
Accepted by The First Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2018). This is a pre-print version (not final version)