Title :
Lower bound on average mean-square error for image restoration
Author_Institution :
Dept. of Electr. & Comput. Eng., Iowa State Univ., Ames, IA, USA
fDate :
2/1/1991 12:00:00 AM
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
Keywords :
interference (signal); picture processing; Cramer-Rao lower bound; algorithmic design; average mean-square error; blur; degradation parameters; image observation models; sensor nonlinearity; signal-dependent noise; signal-independent noise; unbiased image restoration scheme; Covariance matrix; Cramer-Rao bounds; Degradation; Image analysis; Image restoration; Image sensors; Linear matrix inequalities; Parameter estimation; Probability density function; Signal processing;
Journal_Title :
Signal Processing, IEEE Transactions on
