Learning image compressed sensing with sub-pixel convolutional generative adversarial network

Abstract

Compressed sensing (CS) is a new technology to reconstruct image from randomized measurements, but the reconstruction procedure involves a time-consuming iterative optimization. In addition, the reconstruction quality becomes poor in low sampling rate. In order to alleviate these issues of the conventional CS image reconstruction, we propose a novel sub-pixel convolutional generative adversarial network (GAN) to learn compressed sensing reconstruction of images. The generator constructs the sub-pixel convolutional network to learn the explicit mapping from the low-dimensional measurement vector to the high-dimensional reconstruction, in which a compound loss, including reconstruction loss, measurement loss and adversarial loss, is designed to guide the network learning. By means of the adversarial training with discriminator, the generator can learn the inherent image distribution and improve the reconstruction quality. Moreover, the test image can be fast reconstructed by simply passing the low-dimensional measurement vector through the generator network. The proposed algorithm is tested on MNIST, F-MNIST and CelebA datasets, and the experimental results show that it is superior to some state-of-the-art deep learning based and iterative optimization based algorithms, in terms of both time complexity and reconstruction quality.