CG-Net: A Compound Gaussian Prior Based Unrolled Imaging Network

Abstract

In the age of accessible computing, machine intelligence (MI) has become a widely applicable and successful tool in image recognition. With this success, MI has, more recently, been applied to compressive sensing and tomographic imaging. One particular application of MI to image estimation, known as algorithm unrolling, is the implementation of an iterative imaging algorithm as a deep neural network (DNN). Algorithm unrolling has shown improvements in image reconstruction over both iterative imaging algorithms and standard neural networks. Here, we present a least squares iterative image estimation algorithm under the assumption of a Compound Gaussian (CG) prior for the image. The CG prior asserts that the image wavelet coefficients are a nonlinear function of two Gaussians. The developed iterative imaging algorithm is then unrolled into a DNN named CG-Net. After training, CG-Net is shown to be successful in the estimation of image wavelet coefficients from Radon transform measurements.