Compressive Image Recovery Using Recurrent Generative Model

Generative models are considered as the swiss knives for data modelling. In this paper we leverage the recently proposed recurrent generative model, RIDE, for applications like image inpainting and 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 as prior using backpropagation to the inputs and projected gradient method. We propose a entropy thresholding based approach for preserving texture well. Our approach shows comparable results for image inpainting task. It shows superior results in compressive image reconstruction compared to traditional methods D-AMP and TVAL3 which uses global prior of minimizing TV norm.