Convolutional Neural Networks for Non-iterative Reconstruction of Compressively Sensed Images

Abstract

In this paper we have described ReconNet -- a non-iterative algorithm for CS image reconstruction based on CNNs. The advantages of this algorithm are two-fold -- it can be easily implemented while making it 3 orders of magnitude faster than traditional iterative algorithms essentially making reconstruction real-time and it provides excellent reconstruction quality retaining rich semantic information over a large range of measurement rates. We have also discussed novel ways to improve the basic version of our algorithm. We have proposed learning the measurement matrix jointly with the reconstruction network as well as training with adversarial loss based on recently popular GANs. In both cases, we have shown significant improvements in reconstruction quality over a range of measurement rates. Using the ReconNet + KCF pipeline, efficient real-time tracking is possible using CS measurements even at a very low measurement rate of 0.01. This also means that other high-level inference applications such as image recognition can be performed using a similar framework i.e., ReconNet + Recognition from CS measurements. We hope that this work will generate more interest in building practical real-world devices and applications for compressive imaging.