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Proceedings Paper

To denoise or deblur: parameter optimization for imaging systems
Author(s): Kaushik Mitra; Oliver Cossairt; Ashok Veeraraghavan
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Paper Abstract

In recent years smartphone cameras have improved a lot but they still produce very noisy images in low light conditions. This is mainly because of their small sensor size. Image quality can be improved by increasing the aperture size and/or exposure time however this make them susceptible to defocus and/or motion blurs. In this paper, we analyze the trade-off between denoising and deblurring as a function of the illumination level. For this purpose we utilize a recently introduced framework for analysis of computational imaging systems that takes into account the effect of (1) optical multiplexing, (2) noise characteristics of the sensor, and (3) the reconstruction algorithm, which typically uses image priors. Following this framework, we model the image prior using Gaussian Mixture Model (GMM), which allows us to analytically compute the Minimum Mean Squared Error (MMSE). We analyze the specific problem of motion and defocus deblurring, showing how to find the optimal exposure time and aperture setting as a function of illumination level. This framework gives us the machinery to answer an open question in computational imaging: To deblur or denoise?.

Paper Details

Date Published: 7 March 2014
PDF: 6 pages
Proc. SPIE 9023, Digital Photography X, 90230G (7 March 2014); doi: 10.1117/12.2038819
Show Author Affiliations
Kaushik Mitra, Rice Univ. (United States)
Oliver Cossairt, Northwestern Univ. (United States)
Ashok Veeraraghavan, Rice Univ. (United States)


Published in SPIE Proceedings Vol. 9023:
Digital Photography X
Nitin Sampat; Radka Tezaur; Sebastiano Battiato; Boyd A. Fowler, Editor(s)

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