@article{kuehni_ramanath_2006, title={Sensing spectral stimuli: Sensor functions and number}, volume={31}, ISSN={["1520-6378"]}, DOI={10.1002/col.20171}, abstractNote={Abstract}, number={1}, journal={COLOR RESEARCH AND APPLICATION}, author={Kuehni, Rolf G. and Ramanath, Rajeev}, year={2006}, month={Feb}, pages={30–37} }
 @article{karacali_ramanath_snyder_2004, title={A comparative analysis of structural risk minimization by support vector machines and nearest neighbor rule}, volume={25}, ISSN={["1872-7344"]}, DOI={10.1016/j.patrec.2003.09.002}, abstractNote={Support vector machines (SVMs) are by far the most sophisticated and powerful classifiers available today. However, this robustness and novelty in approach come at a large computational cost. On the other hand, nearest neighbor (NN) classifiers provide a simple yet robust approach that is guaranteed to converge to a result. In this paper, we present a technique that combines these two classifiers by adopting a NN rule-based structural risk minimization classifier. Using synthetic and real data, the classification technique is shown to be more robust to kernel conditions with a significantly lower computational cost than conventional SVMs. Consequently, the proposed method provides a powerful alternative to SVMs in applications where computation time and accuracy are of prime importance. Experimental results indicate that the NNSRM formulation is not only computationally less expensive, but also much more robust to varying data representations than SVMs.}, number={1}, journal={PATTERN RECOGNITION LETTERS}, author={Karacali, B and Ramanath, R and Snyder, WE}, year={2004}, month={Jan}, pages={63–71} }
 @article{kuehni_ramanath_2004, title={Comparing observers}, volume={29}, ISSN={["1520-6378"]}, DOI={10.1002/col.20004}, abstractNote={Abstract}, number={3}, journal={COLOR RESEARCH AND APPLICATION}, author={Kuehni, RG and Ramanath, R}, year={2004}, month={Jun}, pages={183–186} }
 @article{ramanath_kuehni_snyder_hinks_2004, title={Spectral spaces and color spaces}, volume={29}, ISSN={["1520-6378"]}, DOI={10.1002/col.10211}, abstractNote={Abstract}, number={1}, journal={COLOR RESEARCH AND APPLICATION}, author={Ramanath, R and Kuehni, RG and Snyder, WE and Hinks, D}, year={2004}, month={Feb}, pages={29–37} }
 @article{ramanath_snyder_2003, title={Adaptive demosaicking}, volume={12}, ISSN={["1017-9909"]}, DOI={10.1117/1.1606459}, abstractNote={the . ; ac Abstract. Digital still color cameras sample the visible spectrum using an array of color filters overlaid on a CCD, such that each pixel samples only one color band. The resulting mosaic of color samples is processed to produce a high-resolution color image, such that a value of a color band not sampled at a certain location is estimated from its neighbors. This is often referred to as ‘‘demosaicking.’’ The human retina has a similar structure, although the distribution of cones is not as regular. Motivated by the human visual system, we propose an adaptive demosaicking technique in the framework of bilateral filtering. This approach provides us with a means to denoise, sharpen, and demosaic the image simultaneously. The proposed method, along with a variety of existing demosaicking strategies, are run on synthetic images and real-world images for comparative purposes. A recently proposed image comparison measure geared specifically toward demosaicking has also been applied to these images to provide a performance measure. © 2003 SPIE and IS&T. [DOI: 10.1117/1.1606459]}, number={4}, journal={JOURNAL OF ELECTRONIC IMAGING}, author={Ramanath, R and Snyder, WE}, year={2003}, month={Oct}, pages={633–642} }
 @article{ramanath_snyder_2002, title={Demosaicking as a bilateral filtering process}, volume={4667}, ISBN={["0-8194-4407-3"]}, ISSN={["0277-786X"]}, DOI={10.1117/12.467984}, abstractNote={Digital Still Color Cameras sample the visible spectrum using an array of color filters overlaid on a CCD such that each pixel samples only one color band. The resulting mosaic of color samples is processed to produce a high resolution color image such that a value of a color band not sampled at a certain location is estimated from its neighbors. This is often referred to as 'demosaicking.' In this paper, we approach the process of demosaicking as a bilateral filtering process which is a combination of spatial domain filtering and filtering based on similarity measures. Bilateral filtering smooths images while preserving edges by means of nonlinear combinations of neighboring image pixel values. A bilateral filter can enforce similarity metrics (such as squared error or error in the CIELAB space) between neighbors while performing the typical filtering operations. We have implemented a variety of kernel combinations while performing demosaicking. This approach provides us with a means to denoise, sharpen and demosaic the image simultaneously. We thus have the ability to represent demosaicking algorithms as spatial convolutions. The proposed method along with a variety of existing demosaicking strategies are run on synthetic images and real-world images for comparative purposes.}, journal={IMAGE PROCESSING: ALGORITHMS AND SYSTEMS}, author={Ramanath, R and Snyder, WE}, year={2002}, pages={236–244} }
 @article{ramanath_snyder_bilbro_sander_2002, title={Demosaicking methods for Bayer color arrays}, volume={11}, ISSN={["1560-229X"]}, DOI={10.1117/1.1484495}, abstractNote={Digital Still Color Cameras sample the color spectrum using a monolithic array of color filters overlaid on a charge coupled device array such that each pixel samples only one color band. The resulting mosaic of color samples is processed to produce a high resolution color image such that the values of the color bands not sampled at a certain location are estimated from its neighbors. This process is often referred to as demosaicking. This paper introduces and compares a few commonly used demosaicking methods using error metrics like mean squared error in the RGB color space and perceived error in the CIELAB color space. © 2002 SPIE and IS&T.}, number={3}, journal={JOURNAL OF ELECTRONIC IMAGING}, author={Ramanath, R and Snyder, WE and Bilbro, GL and Sander, WA}, year={2002}, month={Jul}, pages={306–315} }
 @inbook{ramanath_snyder_hinks_2002, title={Image comparison measure for digital still color cameras}, volume={1}, booktitle={2002 International Conference on Image Processing: proceedings: ICIP: 22-25 September, 2002, Rochester Riverside Convention Center, Rochester, New York, USA: Vol. 1}, publisher={Piscataway, NJ: IEEE}, author={Ramanath, A. R and Snyder, B. W. and Hinks, C. D.}, year={2002}, pages={629–632} }