Robust estimation of wave-front aberration distribution function using invariant wavelet transform profilometry

Abstract: The accuracy and stability of the estimation process of phase aberration directly affects the success of machine vision processes. Therefore, accurate modeling and estimation of noise in the process of recovering of aberration distribution function is of prime importance. In this paper, the noise model is considered as an additive Gaussian translation-independent distribution function. To reduce its impacts on estimating the aberration function, non-linear filtering is done in the wavelet space. Thus, the wavelet coefficients are modified in a way that minimizes the effects of the additive Gaussian noise. Then from the modified wavelet coefficients, the phase aberration function is extracted. Experimental results and the empirical validity of this idea are evaluated. [Copyright &y& Elsevier]