Kaushik Mitra

Computational Imaging @ IITM Lab

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Research

Traditionally, imaging/camera design and imaging inference (image processing, computer vision) have been done independently. However, by jointly designing the imaging optics and inference algorithms, we can significantly enhance the performance of conventional cameras. This is the philosophy behind the fast evolving field of Computational Imaging (CI). My research focuses on the following themes:

Develop theory to explore the performance limits of CI systems
Develop novel CI systems
Explore the promise of data-driven signal models in CI and computer vision

Theory for computational imaging

Framework for analysis of CI systems IEEE TPAMI 2014	Data-driven design for CI systems ICCP 2014	Denoise or deblur: Parameter optimization for imaging systems SPIE Electronic Imaging, 2014	Analyzing computational imaging systems SPIE Newsroom, 2013

Novel CI systems and processing algorithms

Focal-sweep for large aperture time-of-flight cameras ICIP 2016	Spatial Phase-Sweep: Increasing temporal resolution of transient imaging using a light source array ICIP 2016	"Improving resolution of light field using hybrid imagingdesign" ICCP 2014
Compressive epsilon photography SIGGRAPH 2014	A common framework for light field processing CVPR Workshop 2012	Towards compressive camera networks IEEE Computer 2014

Machine learning for computer vision

Deep generative models for computational imaging ICIP 2017, under review in IEEE TCI	Missing data matrix factorization NIPS 2010	Analysis of sparsity based robust regressionalgorihms IEEE TSP 2013	Robust regression using sparse learning techniques ICASSP 2010	Robust RVM regression using sparse outlier model CVPR 2010