“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]
“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.
Critical examination of the slanted-edge method for color SFR measurement reveals inaccuracies in the estimated SFR, due to the use of demosaicing. The proposed method resolves these inaccuracies by eliminating the need for demosaicing during SFR measurement.
INDEX TERMS — Slanted Edge, Color Filter Array, Digital cameras.
“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.
The present work describes an active stereo apparatus that can not only recover scene geometry but also resolve spatial detail beyond the camera optical cutoff. The apparatus is comprised of a camera and a projector whose center-ofperspective is located in the camera pupil plane. The scene is illuminated with warped sinusoidal patterns as opposed to periodic or coded patterns.The findings reported in this work can help design imaging systems that feature improved optical resolution and 3D acquisition capabilities.
INDEX TERMS — OSR, Optical Superresolution, Structured Illumination, Depth maps.
“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.
The present work extends the scope of Optical Super-Resolution to imaging systems with spatially-varying blur, by using sinusoidal illumination. It also establishes that knowledge of the space-variant blur is not a pre-requisite for super-resolution.
INDEX TERMS — Superresolution, Computational Imaging, OSR, Space varying blur.
“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.
Using intelligent resource allocation based on the information content in the imaging systems’ field-of-view for the successful design of a flat-profile multiplexed optical imaging system requires the use of adaptive techniques. This paper describes a model-based technique for determining regions of interest in aerial images using the 2D normalized power spectral density within Gilles’ saliency map estimator. The proposed technique exploits the spatial spectral shape of such natural imagery in a computationally-simple approach that is robust to additive noise. Application of the method to candidate aerial images shows its ability to identify consistent regions of interest for such data.
INDEX TERMS — Optical imaging, image region analysis, information theory, multisensor systems, spectral analysis.
“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.
Successful design of a flat-profile multiplexed optical imaging system requires the use of adaptive techniques to make intelligent resource allocation based on the information content in the imaging system’s field-of-view. This paper explores techniques for rending regions of interest in aerial images using local entropy as a descriptor. A novel method for identifying regions-of-interest in images is developed using the 2D normalized power spectral density within Gilles’ saliency map estimator. Application of the method to candidate aerial images shows its ability to identify consistent regions of interest for such data for varying block sizes and under additive noise.
INDEX TERMS — computational imaging systems, information theory, saliency, image reconstruction.
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.