Scalable Image Compressed Sensing With Generator Networks

Yuanhong Zhong, Chenxu Zhang, Fan Ren, Honggang Kuang, Panliang Tang

Abstract: In the study of image compressed sensing (CS), various priors are explored for regularization to achieve better reconstruction and provide different additional information. However, it is a challenge to combine different priors for better performance. In this article, we propose a novel framework (dubbed SCS-GNet) that integrates the merits of multiple priors for image CS. The framework is composed of a measurement matrix generator network and a reconstruction generator network, in which the implicit image prior is explored in the process of measurement matrix generation, and deep image and sparsity priors are explored in the reconstruction process. The two generator networks are separate in SCS-GNet, and the partial consistency of loss functions is explored for connection to boost the performance of image CS. Different from existing end-to-end joint training deep network-based methods, our measurement matrix generation process is performed separately, while the reconstruction process is training-free. Moreover, a novel training strategy is applied in the process of measurement matrix generation to enable the framework to be available for different measurement rates. Extensive experiments demonstrate that the proposed SCS-GNet achieves adaptive and scalable measurement and reconstruction at different measurement rates and offers competitive performance compared with other state-of-the-art methods.

External IDs:doi:10.1109/tci.2022.3224281