# Publications

 Journal Conference Thesis Others

# JOURNAL PAPERS

## 2017

Geometric model for an independently tilted lens and sensor with application for omnifocus imaging,” Indranil Sinharoy, Prasanna Rangarajan, Marc P. Christensen, OSA Applied Optics Special Issue on Modern Optics, 2017.
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Active computational imaging for circumventing resolution limits at macroscopic scales,” Prasanna Rangarajan, Indranil Sinharoy, Marc P. Christensen, Predrag Milojkovic, OSA Applied Optics Special Issue on Modern Optics, 2017.
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# CONFERENCE PAPERS

## 2016

Omnifocus image synthesis using Lens Swivel,” Indranil Sinharoy, Prasanna Rangarajan, Marc P. Christensen, OSA Topical meeting on Imaging Systems & Applications, 2016.
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## 2014

Optical super resolution using a lattice of light spots,” Prasanna Rangarajan, Indranil Sinharoy, Marc P. Christensen, Predrag Milojkovic, OSA Topical meeting on Computational Imaging, 2014.
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## 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

## 2016

Scheimpflug with Computational Imaging to Extend the Depth of Field of Iris Recognition Systems,” Indranil Sinharoy, Southern Methodist University, December 2016.(PhD dissertation)
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[ABSTRACT]
Despite the enormous success of iris recognition in close-range and well-regulated spaces for biometric authentication, it has hitherto failed to gain wide-scale adoption in less controlled, public environments. The problem arises from a limitation in imaging called the depth of field (DOF): the limited range of distances beyond which subjects appear blurry in the image. The loss of spatial details in the iris image outside the small DOF limits the iris image capture to a small volume–the capture volume. Existing techniques to extend the capture volume are usually expensive, computationally intensive, or afflicted by noise. Is there a way to combine the classical Scheimpflug principle with the modern computational imaging techniques to extend the capture volume? The solution we found is, surprisingly, simple; yet, it provides several key advantages over existing approaches. Our method, called Angular Focus Stacking (AFS), consists of capturing a set of images while rotating the lens, followed by registration, and blending of the in-focus regions from the images in the stack. The theoretical underpinnings of AFS arose from a pair of new and general imaging models we developed for Scheimpflug imaging that directly incorporates the pupil parameters. The model revealed that we could register the images in the stack analytically if we pivot the lens at the center of its entrance pupil, rendering the registration process exact. Additionally, we found that a specific lens design further reduces the complexity of image registration making AFS suitable for real-time performance. We have demonstrated up to an order of magnitude improvement in the axial capture volume over conventional image capture without sacrificing optical resolution and signal-to-noise ratio. The total time required for capturing the set of images for AFS is less than the time needed for a single-exposure, conventional image for the same DOF and brightness level. The net reduction in capture time can significantly relax the constraints on subject movement during iris acquisition, making it less restrictive.

## 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.

# OTHERS

Non-peer reviewed papers, technical articles, pre-prints.

## 2016

Geometric model of image formation in Scheimpflug cameras,” Indranil Sinharoy, Prasanna Rangarajan, and Marc P. Christensen, PeerJ Preprints 4:e1887v1.

## 2015

Array ray tracing in Zemax OpticStudio from Python using the DDE extension,” Indranil Sinharoy, Knowledgebase article for Zemax.

## 2014

Talking to ZEMAX from Python using PyZDDE,” Indranil Sinharoy, Knowledgebase article for Zemax.

## 2004

Image Denoising Using Modified Neighshrink Algorithm,” Indranil Sinharoy, Tech. Report, Southern Methodist University 2012.
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