@inproceedings{1b78dcc788c8471c8d161d5c009f9784,
title = "Compressive image recovery using recurrent generative model",
abstract = "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 can handle global multiplexing in 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.",
keywords = "Compressive imaging, Deep learning, Generative models, LSTMs, MAP inference",
author = "Akshat Dave and Anil Kumar and Vadathya and Kaushik Mitra",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 24th IEEE International Conference on Image Processing, ICIP 2017 ; Conference date: 17-09-2017 Through 20-09-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICIP.2017.8296572",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "1702--1706",
booktitle = "2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings",
}