Anil Kumar Vadathya

I am a master's student with Dr. Kaushik Mitra at IIT Madras, where I work on computational imaging, deep learning and image processing. At Computational Imaging lab , I've worked on deep learning based solutions for image restoration in compressive imaging setups like Single Pixel Camera, compressive light field reconstruction and RGBD priors. Prior to this I got my B. Tech in Electronics and Communications from RGUKT, Basar in 2015.

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.

Email  /  CV  /  Google Scholar


  • (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.


A Deep Learning Framework for Light Field Reconstruction From Focus-Defocus Pair:
a minimal hardware approach

Anil Kumar Vadathya, Sharath Girish, Kaushik Mitra
Computational Cameras and Displays (CCD) workshop, CVPR, 2018
poster /

We provide a learning algorithm for light field reconstruction from a conventional camera.

Solving Inverse Computational Imaging Problems using Deep Pixel-level Prior
Akshat Dave, Anil Kumar V., Ramana Subramanyam, Rahul Baburajan, Kaushik Mitra
Under review at IEEE Trans. on Computational Imaging
Accepted at Deep Generative Models applications (TADGM) workshop at ICML, 2018!
arxiv / code

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.


Learning Light Field Reconstruction from a Single Coded Image
Anil Kumar V., Saikiran C., Gautham R., Vijayalakshmi Kanchana, Kaushik Mitra
Asian Conference on Pattern Recognition, 2017
project page / poster

Using deep neural networks we reconstruct full sensor resolution light field from a single coded image. Our approach involves depth based rendering where depth is learnt in an unsupervised manner.

Compressive Image Recovery Using Recurrent Generative Model
Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra
International Conference on Image Processsing (ICIP), 2017
project page / github / poster

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.

Denoising High Density Gene Expression in Whole Mouse Brain Images
Mayug Maniparambil, Anil Kumar V., Kannan U. V., Kaushik Mitra, Pavel Osten
Society for Neuroscience (SfN), 2017

We use a deep autoencoder with adversarial loss for denoising the gene expression to improve the registration accuracy.

Butterfly Communication Strategies: A prospect for soft-computing techniques
Sowmya Charugundla, Anjumara Shaik, Chakravarthi Jada, Anil Kumar Vadathya
International Joint Conference on Neural Networks (IJCNN), 2014
BMO code for three peaks function.

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.

ROBOG Autonomously Navigating Outdoor Robo-Guide
Kranthi Kumar R., Irfan Feroz G. M., Chakravarthi Jada, Harish Y., Anil Kumar V.
International Conference on Swarm, Evolutionary, and Memetic Computing, 2014

Neural networks are used to learn the navigation information. For outdoor navigation, we propose an image processing pipeline for road detection.


Autoregressive RGBD priors
Anil Kumar Vadathya, May 2017



Face Quality Assessment using Restricted Boltzmann Machines (RBMs)
Anil Kumar Vadathya, May 2014

We use RBMs to model basis of atoms for facial features and use the basis to evaluate facial image quality.

Thanks to this guy for allowing me to use his template!