Comparison on performance of some representative and recent filtering methods in electronic speckle pattern interferometry

Methods of phase reconstruction for time-averaging electronic speckle pattern interferometry

“…In general, the Fourier transform of a small part of an image is multiplied by a filter function pixel-bypixel, and then transformed back to the spatial domain. This procedure is done for the whole image, and the filter function defines the method and the effect of filtering [22].…”

Variational image decomposition for estimation of fringe orientation and density from electronic speckle pattern interferometry fringe patterns with greatly variable density

“…Fringe orientation and density are important properties of fringes for directing electronic speckle pattern interferometry (ESPI) fringes processing such as image filtering [1][2][3] and skeleton extraction [4]. Obviously, errors in estimation of fringe orientation or density will affect noise reduction and as a consequence the accuracy of the fringe analysis.…”

Vibration measurement based on electronic speckle pattern interferometry and radial basis function

“…A multitude of methods have been used to remove the noise, such as mean filter, median filter, low-pass Fourier filter, partial differential equation (PDE) based method [16], spin filters [17] and Fourier transform based method [18]. Radial basis function (RBF) method, which is a powerful mathematical tool for scatter data approximation [19], can also be applied to filter the image.…”