Roughness being a crucial feature of the surface texture is estimated through numerous techniques. The laser speckle imaging method has emerged as an efficient non-contact tool in the regime of surface roughness measurement techniques. This work presents singular value decomposition-based roughness measurement using objective speckle patterns of the machined surfaces. The surface roughness is quantified as a function of a proposed metric which is the exponential decay rate of the singular values associated with the speckle pattern.

Fringe pattern denoising is of prime importance in phase demodulation, especially for a single fringe pattern in speckle interferometry. A fringe speckle noise removal algorithm using the Kalman filter is proposed. The conventional linear Kalman filter is implemented with a fixed value of the process and measurement noise covariances; the adaptive Kalman filter is implemented with the process, and measurement noise covariances are estimated in an adaptive manner.