Conventional compressive sensing (CS) reconstruction is very slow for its characteristic of solving an optimization problem. Convolu- tional neural network can realize fast processing while achieving compa- rable results. While CS image recovery with high quality not only de- pends on good reconstruction algorithms, but also good measurements. In this paper, we propose an adaptive measurement network in which measurement is obtained by learning. The new network consists of a fully-connected layer and ReconNet. The fully-connected layer which has low-dimension output acts as measurement. We train the fully-connected layer and ReconNet simultaneously and obtain adaptive measurement. Because the adaptive measurement fits dataset better, in contrast with random Gaussian measurement matrix, under the same measuremen- t rate, it can extract the information of scene more efficiently and get better reconstruction results. Experiments show that the new network outperforms the original one.
@article{arxiv.1710.01244,
title = {Adaptive Measurement Network for CS Image Reconstruction},
author = {Xuemei Xie and Yuxiang Wang and Guangming Shi and Chenye Wang and Jiang Du and Zhifu Zhao},
journal= {arXiv preprint arXiv:1710.01244},
year = {2017}
}