Speckle Denoising By The Family Of Non-Local Means Methods

We demonstrate the performance of nonlocal means (NLM) and its related adaptive kernel-based methods for speckle denoising for the intensity and phase images acquired from the digital speckle pattern interferometric (DSPI) and digital holographic interferometric (DHI) techniques, respectively. The speckle denoise capabilities of NLM and its variant denoising methods such as NLM-average (NLM-av), NLM-local polynomial regression (NLM-PLR), and NLM-shape adaptive patches (NLM-SAP), and various NLM-reprojection schemes are implemented on simulated. Their performances are quantified on the basis of two metric criteria - peak signal-to-noise ratio (PSNR) and the image quality index (Q). The effectiveness of these denoising methods is compared with other existing speckle denoising methods. The obtained results suggest that these denoising methods have the ability to denoise speckles from the DSPI/DHI fringes and provide better visual and quantitative results.