Compressive Image Recovery Using Recurrent Generative Model
This work addresses image reconstruction in compressive sensing, which is important for applications like medical imaging or surveillance, but it appears incremental as it builds on existing generative models.
The paper tackled the ill-posed problem of reconstructing images from compressively sensed measurements by using a recurrent generative model (RIDE) as an image prior, achieving superior reconstructions compared to methods like D-AMP and TVAL3 on simulated and real data.
Reconstruction of signals from compressively sensed measurements is an ill-posed problem. In this paper, we leverage the recurrent generative model, RIDE, as an image prior for compressive image reconstruction. Recurrent networks can model long-range dependencies in images and hence are suitable to handle global multiplexing in reconstruction from compressive imaging. We perform MAP inference with RIDE using back-propagation to the inputs and projected gradient method. We propose an entropy thresholding based approach for preserving texture in images well. Our approach shows superior reconstructions compared to recent global reconstruction approaches like D-AMP and TVAL3 on both simulated and real data.