Wrapped phase denoising using adaptive Kalman smoother algorithm

Abstract

A wrapped phase pattern denoising algorithm is proposed based on the adaptive Kalman filtering. The exponential phase field (EPF) corresponding to the noisy wrapped phase pattern is considered as the measurement data. Row-wise and column-wise phase denoising is performed in a sequential manner. In each row/column, the EPF is denoised using the Kalman filter considering it as a spatially varying auto-regressive process. For achieving measurement independent, automatic phase denoising, the system and measurement noise covariances are adaptively estimated in the implementation of Kalman filter. The proposed formulation also allows simultaneous denoising of multiple rows/columns to reduce the computation cost. The amplitude of denoised EPF can be used as a phase quality metric which can aid in the subsequent phase unwrapping operation. The denoising performance is quantitatively evaluated using simulation results. Experimental results are provided to further substantiate the practical applicability of the proposed method.