# Publications

 Journal Conference Thesis Others

# JOURNAL PAPERS

## 2012

Structured illumination optical super-resolution with space-variant perspective imaging systems,” Prasanna Rangarajan, Indranil Sinharoy, Manjunath Somayaji, Vikrant R. Bhakta, Predrag Milojkovic and Marc P. Christensen. [Currently as manuscript, in preparation for submission to Optics Express]

# CONFERENCE PAPERS

## 2012

A critical review of the slanted-edge method for color SFR measurement,” Prasanna Rangarajan, Indranil Sinharoy, Marc P. Christensen, Predrag Milojkovic, OSA Topical meeting on Imaging Systems & Applications, 2012.
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## 2011

Pushing the limits of digital imaging using Structured Illumination,” Prasanna Rangarajan, Indranil Sinharoy, Panos Papamichalis, Marc P. Christensen , Proc. 13th IEEE International Conference on Computer Vision, ICCV 2011.
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Space-Variant Optical Super-Resolution using Sinusoidal Illumination,” Prasanna Rangarajan, Vikrant R. Bhakta, Indranil Sinharoy, Manjunath Somayaji and Marc P. Christensen, Computational Optical Sensing and Imaging (COSI),Toronto, Canada, July, 2011.
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## 2008

Model-Based Region-Of-Interest estimation for adaptive resource allocation in multi-aperture imaging systems,” Indranil Sinharoy, Scott C. Douglas, Dinesh Rajan, Marc P. Christensen, IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), Las Vegas, Nevada,pp. 1961-1964, Apr. 2008.
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## 2007

Region-of-interest estimation for adaptive resource allocation in multi-aperture imaging systems,” I. Sinharoy, S.C. Douglas, IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), Honolulu, HI, vol. 2, pp. 597-600, Apr. 2007.
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# THESIS

## 2006

Region-of-interest estimation for adaptive resource allocation in multi-aperture imaging systems,” Indranil Sinharoy, Southern Methodist University, December 2006.(Master’s thesis)
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[ABSTRACT]
Scaling down traditional optical imaging systems to enhance their form factor presents fundamental challenges in terms of loss of resolution and a decrease in the optical SNR due to the reduced light gathering ability of such scaled imagers. Computational imaging systems can address these issues through joint optimization of their optics and signal processing subsystems. One class of computational imagers is the thin, flat-profile multiplexed imaging system, which uses a combination of several scaled individual imaging units to capture a number of low-resolution images that are then digitally processed to reconstruct a high resolution version of the observed scene.
The performance of multiplexed imagers may be enhanced through the use of adaptive techniques wherein imager resource utilization is maximized through intelligent resource allocation based on the information content in the scene. The body of work laid out in this thesis describes techniques to find regions of interest in a scene and serves to enhance the efficiency of resource allocation in adaptive multiplexed imaging systems. The power spectral density (PSD) is used to derive local entropy maps of input scenes towards identification of regions of interest. Empirical evidence supporting the superiority of PSD-based saliency maps over their histogram-based counterparts in terms of relative local saliency representation of various regions within a scene is provided. Statistical analysis of noise in a scientific-grade digital camera shows that the noise power in images increased with pixel intensity indicating Poisson noise characteristics. A numerically fast and efficient method for computing model-based local saliency maps is presented, and its performance is evaluated in terms of the number of adders, multipliers and table lookups.

Coming soon.