To denoise or deblur: Parameter optimization for imaging systems

Kaushik Mitra, Oliver Strides Cossairt, Ashok Veeraraghavan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Digital Photography X
PublisherSPIE
ISBN (Print)9780819499400
DOIs
StatePublished - 2014
EventDigital Photography X - San Francisco, CA, United States
Duration: Feb 3 2014Feb 5 2014

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9023
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherDigital Photography X
Country/TerritoryUnited States
CitySan Francisco, CA
Period2/3/142/5/14

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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