Optimal signal-adaptive maximum likelihood filter for enhancement of defects in eddy current C-scan images

True estimation of the boundary and size of defects are major problems in eddy current (EC) non-destructive evaluation of conductive materials. EC image processing techniques can be used for better estimation of defect details. Because of non-stationary nature of EC C-scan images and same intensities of noise and defect histograms, the linear techniques do not produce good results. In this paper a non-linear signal-adaptive filter based on maximum likelihood (SAML) criterion is designed and successfully used for defect detection. The model of EC noise in this filter is assumed to be non-zero mean complex Gaussian process. The desired model of SAML (MSAML) filter is also modified to further reduce the probability of error and enhance defect details and boundary realization. Simulated and experimental results demonstrate the successful performance of the SAML and MASML filters in estimation of defect details and noise removal.