Our project titled "Deep Generative Models for Computational Imaging" received Qualcomm Innovation Fellowship (QInF), India 2016 and Super winner for the consecutive year 2017 among four prestigious projects.
(Nov 12th, 18) Our work on solving inverse problems using deep pixel-level prior is accepted at IEEE Trans. on Computational Imaging (TCI)!
(Nov 10th, 18) Joined as Research Engineer at Rice Univeristy, Houston, US!
Deep pixel-level prior for inverse computational imaging is accepted at TADGM workshop, ICML 2018! (June 15th, 18)
Poster on light field reconstruction from focus-defocus pair at CCD workshop, CVPR 2018! (May 30th, 18)
Poster demo on solving inverse problems using deep pixel-level prior at ICCP 2018, CMU, US! (Apr 25th, 18)
Our QInF 2016 got renewal to 2017! (among four prestigious projects)
I'm interested in computational photography, image processing, optimization, and machine learning. Much of my recent work has focused on using deep generative modesl for inverse problems in computational imaging.
We use a deep autoregressive image prior PixelCNN++ for solving inverse imaging problems. Since autoregressive nature explicitly models pixel level dependencies it reconstruct pixel level details much better than existing state of the art methods.
We use a deep recurrent image prior, RIDE, which can model long range dependencies in images very well. Using this for compressive image recovery we show much better reconstructions especially at lower measurement rates.
We show mathematical models of communication mechanisms debloyed by butterflies. This work is extended to an optimization algorithm, Butterfly Mating Optimization (BMO), Jada et al. 2015. We applied it for image clustering application.