Patch-wise denoising of phase fringe patterns based on matrix enhancement

Abstract

We propose a new approach for the denoising of a phase fringe pattern recorded in an optical interferometric setup. The phase fringe pattern which is generally corrupted by high amount of speckle noise is first converted into an exponential phase field. This phase field is divided into number of overlapping patches. Owing to the small size of each patch, the presence of a simple structure of the interference phase is assumed in it. Accordingly, the singular value decomposition (SVD) of the patch allows to separate the signal and noise components effectively. The patch is reconstructed only with the signal component. In order to further improve the robustness of the proposed method, an enhanced data matrix is generated using the patch and the SVD of this enhanced matrix is computed. The matrix enhancement results in an increased dimension of the noise subspace which thus accommodates more amount of noise component. Reassignment of the filtered pixels of the preceding patch in the current patch improves the noise filtering accuracy. The fringe denoising capability in function of the noise level and the patch size is studied. Simulation and experimental results are provided to demonstrate the practical applicability of the proposed method.

A filtering scheme for phase map of active tissue interface strain based on adaptive variational mode decomposition

“…The EMD nonstationary signal approach is able to adaptively decompose complex multi-component signals into numerous components 12 . For the noise of DSPI phase maps, several de-noised methods have been proposed in the past years, such as, Fourier transformation method, average filtering method, multi-scale filtering methods, median filtering method, partial differential filtering method, etc [13][14][15][16][17] . When the Fourier transformation is used in the noise reduction, the window function selection is required 18 .…”

Non-invasive deformation metrology using subspace analysis in digital holographic interferometry

“…A major limitation for phase retrieval in DHI is the presence of severe noise, which deteriorates the performance of the aforesaid methods. This usually necessitates the application of additional filtering operations [19][20][21] to remove noise; but this procedure introduces additional computational component and risk of smearing the fringe details. Similarly, computational denoising approaches based on deep learning [22,23] have also been proposed.…”