Lower bound on average mean-square error for image restoration

  • Authors:

  • H. Hung

  • Affiliations:

  • Dept. of Electr. & Comput. Eng., Iowa State Univ., Ames, IA

  • Venue:
  • IEEE Transactions on Signal Processing
  • Year:
  • 1991

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Abstract

An average mean-square error bound that is applicable to general image observation models involving degradations of blur, signal-dependent and signal-independent noise, and sensor nonlinearity is derived. A Cramer-Rao lower bound on average mean-square errors for any unbiased image restoration scheme is derived. This bound is analytically expressed as a function of degradation parameters of imaging systems. Potential performance improvements by incorporating signal-dependent noise or sensor nonlinearity into algorithmic design are discussed