7 March 2014 To denoise or deblur: parameter optimization for imaging systems
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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?.
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Kaushik Mitra, Oliver Cossairt, Ashok Veeraraghavan, "To denoise or deblur: parameter optimization for imaging systems", Proc. SPIE 9023, Digital Photography X, 90230G (7 March 2014); doi: 10.1117/12.2038819; https://doi.org/10.1117/12.2038819
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