Silvio Montresor

Quantitative appraisal for noise reduction in digital holographic phase imaging.

This paper discusses on a quantitative comparison of the performances of different advanced algorithms for phase data de-noising. In order to quantify the performances, several criteria are proposed: the gain in the signal-to-noise ratio, the Q index, the standard deviation of the phase error, and the signal to distortion ratio. The proposed methodology to investigate de-noising algorithms is based on the use of a realistic simulation of noise-corrupted phase data. A database including 25 fringe patterns divided into 5 patterns and 5 different signal-to-noise ratios was generated to evaluate the selected de-noising algorithms. A total of 34 algorithms divided into different families were evaluated. Quantitative appraisal leads to ranking within the considered criteria. A fairly good correlation between the signal-to-noise ratio gain and the quality index has been observed. There exists an anti-correlation between the phase error and the quality index which indicates that the phase errors are mainly structural distortions in the fringe pattern. Experimental results are thoroughly discussed in the paper.

Comparison of time-frequency estimators for peripheral embolus detection

Recently, a time-frequency processing of peripheral arterial Doppler signals, based on the spectrogram, was proposed to detect automatically high-intensity transient signal. Three time-frequency representations, the smooth-pseudo-Wigner-Ville, the Choï-Williams and the cone-kernel distributions were compared with the spectrogram, following the detection scheme previously reported. The results showed that the spectrogram provided the best compromises between false-detection and no-detection compared with the other time-frequency representations.

The Narrow Band Hypothesis: An Interesting Approach for High-Intensity Transient Signals (HITS) Detection

We propose a new approach to detect microemboli automatically using the narrow band hypothesis. An initial database of 560 peripheral arterial Doppler high-intensity transient signals (HITS) was created to study microemboli and to define the normal limits to be used in our method. When a HITS occurs, our approach consists of modelling the Doppler signal using amplitude and frequency wave modulation. A threshold was defined experimentally using this database and then applied to 38 recordings from 12 patients. Using another database, six expert Doppler users reported 140, 176, 155, 161, 161 and 146 HITS, corresponding to a total of 197 different observed HITS. When an event was detected by 6, 5, 4, 3, 2 and 1 of the observers, the sensitivity of the automatic detection was 94.8%, 75.9%, 55.6%, 42.9%, 30% and 0%, respectively. The sensitivity of our automatic detection thus is highly associated with the likelihood (defined as the ratio of observers in agreement to the total number of observers) of an event: r = 0.99 for p < 0.0001. Although future research would result in improvement of the specificity, the narrow band hypothesis appears to be a promising technique for the detection of HITS.

Spectrogram analysis of arterial Doppler signals for off-line automated HITS detection

Recently, a time processing of arterial Doppler signals was proposed to detect automatically high-intensity transient signals (HITS). This technique provided satisfactory detection results, but was not always constantly accurate, particularly with high-resistance blood velocity profiles. A time-frequency processing, based on the spectrogram, is presented to detect the presence of emboli in the arterial Doppler signals. The method uses the narrow-band hypothesis and extracts the detection criterion from the time-frequency representation (TFR). A first database of 560 peripheral arterial Doppler HITS was created to study microemboli and to define the normal limits to be used in our method. A threshold was experimentally defined using this database, and then applied to 38 recordings from 12 patients. Using another database, 6 human expert Doppler users reported 140, 176, 155, 161, 161 and 146 HITS, corresponding to a total of 197 different observed HITS. When an event was detected by at least 6, 5, 4, 3, 2 and 1 of the observers, sensitivity of the automatic detection was 93.9, 91.7, 89.6, 88.7, 84.7 and 73.1%, respectively. The sensitivity of our automatic detection is, thus, highly associated with the number of observers in agreement. A preliminary experiment has been performed to test the method in the case of long recording duration. In 15 patients, 6 h 24 min of recordings have been analyzed. The proposed automated processing provided an overall sensibility of 91.5%. The present work shows that this detection scheme preserves good sensibility and improves the positive predictive value compared with the time-processing recently proposed.

Phase calibration unwrapping algorithm for phase data corrupted by strong decorrelation speckle noise

Robust phase unwrapping in the presence of high noise remains an open issue. Especially, when both noise and fringe densities are high, pre-filtering may lead to phase dislocations and smoothing that complicate even more unwrapping. In this paper an approach to deal with high noise and to unwrap successfully phase data is proposed. Taking into account influence of noise in wrapped data, a calibration method of the 1st order spatial phase derivative is proposed and an iterative approach is presented. We demonstrate that the proposed method is able to process holographic phase data corrupted by non-Gaussian speckle decorrelation noise. The algorithm is validated by realistic numerical simulations in which the fringe density and noise standard deviation is progressively increased. Comparison with other established algorithms shows that the proposed algorithm exhibits better accuracy and shorter computation time, whereas others may fail to unwrap. The proposed algorithm is applied to phase data from digital holographic metrology and the unwrapped results demonstrate its practical effectiveness. The realistic simulations and experiments demonstrate that the proposed unwrapping algorithm is robust and fast in the presence of strong speckle decorrelation noise.

Acoustic emission signal denoising to improve damage analysis in glass fibre-reinforced composites

In this contribution, continuous wavelet denoising technique is developed to enhance the signal to noise ratio of acoustic emission (AE) signals. The time–frequency characteristics of the recorded noise are first determined by calculating the noise wavelet coefficients. Then, an algorithm able to proceed denoising by extracting noise features from those corresponding to AE signals is developed. The denoising procedure is applied to get meaningful signals when the detection threshold is decreased from 30dB to 25dB. Furthermore, the spectral flatness measure was applied to eliminate signals originating from noise. Experiments were conducted on cross-ply composite specimens [ ± 45°]6S to examine the efficacy of the proposed technique. The denoised signals were analysed using the continuous wavelet transform and were also analysed in terms of the dominant frequency band which was processed by a fast Fourier transform. Results show that denoised low-amplitude AE hits are well correlated with high-amplitude AE hits (A ≥ 30 dB) in terms of damage characterisation.

Error analysis for noise reduction in 3D deformation measurement with digital color holography

This paper presents an analysis of phase errors generated by advanced noise removal algorithms applied to phase measurements obtained from digital holographic interferometry. The output phase error is analyzed considering two contributions: the error generated by the denoising method and the error due to phase noise. In addition, a third type of error allows quantifying the ability of any algorithm to estimate the initial noise level. A comparison of several denoising algorithms is presented to independently assess the three types of errors from a data set of simulated fringe patterns with controlled realistic noise. The results exhibit the best method for which the output error, together with the method error, is the smallest one. Knowing the error method is advantageously applied to digital color holography experiments in which deformation measurements of an object submitted to mechanical strength are given with a percentage of error due to the denoising algorithm.

Refocus criterion based on maximization of the coherence factor in digital three-wavelength holographic interferometry

This Letter presents an alternative approach for image refocusing in digital Fresnel holography. In the literature, a large majority of reported focus detection criteria is based on amplitude contrast or phase contrast. We propose a focus detection criterion based on the speckle phase coherence factor. This factor reaches its maximum value at the best focus distance. At any reconstruction distance, estimation of the coherence factor is based on robust noise estimation from anisotropic diffusion. We propose a theoretical relation to estimate the coherence factor from the measured standard deviation. Experimental results for the case of three-color holographic imaging and interferometry are provided. Experimental results show the suitability of the proposed approach for image plane refocusing.

Robust processing of phase dislocations based on combined unwrapping and inpainting approaches

This Letter proposes a robust processing of phase dislocations to recover continuous phase maps. The approach is based on combined unwrapping and inpainting methods. Phase dislocations are determined using an estimator based on the second order phase gradient. The algorithm is validated using a realistic simulation of phase dislocations, and the phase restoration exhibits only weak errors. A comparison with other inpainting algorithms is also provided, demonstrating the suitability of the approach. The approach is applied to experimental data from off-axis digital holographic interferometry. The phase dislocation from phase data from a wake flow at Mach 0.73 are identified and processed. Excellent phase restoration can be appreciated.

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

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.