Investigation of de-noising processing on the contrast transfer function in digital holographic imaging

Abstract

This paper presents the analysis of the influence of de-noising algorithms from the point of view of the contrast transfer function (CTF). The study focuses on amplitude images from digitally recorded holograms of the USAF target. In order to assess the evaluations, a database is constituted with experimental images extracted from 20 holograms acquired with a random illumination. Then, using the 20 amplitude images, 20 other images are computed in order to get images with increasing SNR (signal to noise ratio). In order to study the influence of de-noising algorithms on the CTF, 16 ROIs (region of interest) are selected in the USAF target. Each ROI corresponds to a specific pattern at a given spatial resolution. For the evaluations, 34 denoising algorithms were chosen considering their efficiency in image processing and digital holography. We choose advanced methods as stationary wavelet transform based algorithm with Daubechies and symlets wavelet, curvelets, contourlets, BM3D algorithm (state of the art in the image processing) and NL-means algorithms; in addition, we consider classical methods such as Wiener, median and Gauss filtering, anisotropic filtering and Frost filter which was widely used in SAR (synthetic aperture radar) imaging. From the ROIs, the CTF is evaluated. Then, we provide ranking of de-noising algorithms by considering the measured CTF. The variation of the CTF versus the input SNR is analysed for each algorithm.