TY - GEN
T1 - To denoise or deblur
T2 - Digital Photography X
AU - Mitra, Kaushik
AU - Cossairt, Oliver Strides
AU - Veeraraghavan, Ashok
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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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U2 - 10.1117/12.2038819
DO - 10.1117/12.2038819
M3 - Conference contribution
AN - SCOPUS:84901793452
SN - 9780819499400
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Proceedings of SPIE-IS and T Electronic Imaging - Digital Photography X
PB - SPIE
Y2 - 3 February 2014 through 5 February 2014
ER -