The Experts below are selected from a list of 15753 Experts worldwide ranked by ideXlab platform
Hugo J B Marroux - One of the best experts on this subject based on the ideXlab platform.
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source noise suppression in attosecond transient absorption spectroscopy by Edge Pixel referencing
Optics Express, 2021Co-Authors: Romain Geneaux, Hungtzu Chang, Adam M Schwartzberg, Hugo J B MarrouxAbstract:Attosecond transient absorption spectroscopy (ATAS) is used to observe photoexcited dynamics with outstanding time resolution. The main experimental challenge of this technique is that high-harmonic generation sources show significant instabilities, resulting in sub-par sensitivity when compared to other techniques. This paper proposes Edge-Pixel referencing as a means to suppress this noise. Two approaches are introduced: the first is deterministic and uses a correlation analysis, while the second relies on singular value decomposition. Each method is demonstrated and quantified on a noisy measurement taken on WS2 and results in a fivefold increase in sensitivity. The combination of the two methods ensures the fidelity of the procedure and can be implemented on live data collection but also on existing datasets. The results show that Edge-referencing methods bring the sensitivity of ATAS near the detector noise floor. An implementation of the post-processing code is provided to the reader.
M V Swain - One of the best experts on this subject based on the ideXlab platform.
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missing surface estimation based on modified tikhonov regularization application for destructed dental tissue
IEEE Transactions on Image Processing, 2018Co-Authors: Mojtaba Lashgari, Mahdi Shahmoradi, Hossein Rabbani, M V SwainAbstract:Estimation of missing digital information is mostly addressed by 1- or 2-D signal processing methods; however, this problem can emerge in multi-dimensional data including 3-D images. Examples of 3-D images dealing with missing Edge information are often found using dental micro-CT, where the natural contours of dental enamel and dentine are partially dissolved or lost by caries. In this paper, we present a novel sequential approach to estimate the missing surface of an object. First, an initial correct contour is determined interactively or automatically, for the starting slice. This contour information defines the local search area and provides the overall estimation pattern for the Edge candidates in the next slice. The search for Edge candidates in the next slice is performed in the perpendicular direction to the obtained initial Edge in order to find and label the corrupted Edge candidates. Subsequently, the location information of both initial and nominated Edge candidates are transformed and segregated into two independent signals (X-coordinates and Y-coordinates) and the problem is changed into error concealment. In the next step, the missing samples of these signals are estimated using a modified Tikhonov regularization model with two new terms. One term contributes in the denoising of the corrupted signal by defining an estimation model for a group of mildly destructed samples, and the other term contributes in the estimation of the missing samples with the highest similarity to the samples of the obtained signals from the previous slice. Finally, the reconstructed signals are transformed inversely to Edge Pixel representation. The estimated Edges in each slice are considered as initial Edge information for the next slice, and this procedure is repeated slice by slice until the entire contour of the destructed surface is estimated. The visual results as well as quantitative results (using both contour-based and area-based metrics) for seven image data sets of tooth samples with considerable destruction of the dentin-enamel junction demonstrates that the proposed method can accurately interpolate the shape and the position of the missing surfaces in computed tomography images in both two and 3-D (e.g., 14.87 ± 3.87 $\mu \text{m}$ of mean distance (MD) error for the proposed method versus 7.33 ± 0.27 $\mu \text{m}$ of MD error between human experts and 1.25± ~ 0 % error rate (ER) of the proposed method versus 0.64± ~ 0 % of ER between human experts (~1% difference)).
Romain Geneaux - One of the best experts on this subject based on the ideXlab platform.
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source noise suppression in attosecond transient absorption spectroscopy by Edge Pixel referencing
Optics Express, 2021Co-Authors: Romain Geneaux, Hungtzu Chang, Adam M Schwartzberg, Hugo J B MarrouxAbstract:Attosecond transient absorption spectroscopy (ATAS) is used to observe photoexcited dynamics with outstanding time resolution. The main experimental challenge of this technique is that high-harmonic generation sources show significant instabilities, resulting in sub-par sensitivity when compared to other techniques. This paper proposes Edge-Pixel referencing as a means to suppress this noise. Two approaches are introduced: the first is deterministic and uses a correlation analysis, while the second relies on singular value decomposition. Each method is demonstrated and quantified on a noisy measurement taken on WS2 and results in a fivefold increase in sensitivity. The combination of the two methods ensures the fidelity of the procedure and can be implemented on live data collection but also on existing datasets. The results show that Edge-referencing methods bring the sensitivity of ATAS near the detector noise floor. An implementation of the post-processing code is provided to the reader.
Mojtaba Lashgari - One of the best experts on this subject based on the ideXlab platform.
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missing surface estimation based on modified tikhonov regularization application for destructed dental tissue
IEEE Transactions on Image Processing, 2018Co-Authors: Mojtaba Lashgari, Mahdi Shahmoradi, Hossein Rabbani, M V SwainAbstract:Estimation of missing digital information is mostly addressed by 1- or 2-D signal processing methods; however, this problem can emerge in multi-dimensional data including 3-D images. Examples of 3-D images dealing with missing Edge information are often found using dental micro-CT, where the natural contours of dental enamel and dentine are partially dissolved or lost by caries. In this paper, we present a novel sequential approach to estimate the missing surface of an object. First, an initial correct contour is determined interactively or automatically, for the starting slice. This contour information defines the local search area and provides the overall estimation pattern for the Edge candidates in the next slice. The search for Edge candidates in the next slice is performed in the perpendicular direction to the obtained initial Edge in order to find and label the corrupted Edge candidates. Subsequently, the location information of both initial and nominated Edge candidates are transformed and segregated into two independent signals (X-coordinates and Y-coordinates) and the problem is changed into error concealment. In the next step, the missing samples of these signals are estimated using a modified Tikhonov regularization model with two new terms. One term contributes in the denoising of the corrupted signal by defining an estimation model for a group of mildly destructed samples, and the other term contributes in the estimation of the missing samples with the highest similarity to the samples of the obtained signals from the previous slice. Finally, the reconstructed signals are transformed inversely to Edge Pixel representation. The estimated Edges in each slice are considered as initial Edge information for the next slice, and this procedure is repeated slice by slice until the entire contour of the destructed surface is estimated. The visual results as well as quantitative results (using both contour-based and area-based metrics) for seven image data sets of tooth samples with considerable destruction of the dentin-enamel junction demonstrates that the proposed method can accurately interpolate the shape and the position of the missing surfaces in computed tomography images in both two and 3-D (e.g., 14.87 ± 3.87 $\mu \text{m}$ of mean distance (MD) error for the proposed method versus 7.33 ± 0.27 $\mu \text{m}$ of MD error between human experts and 1.25± ~ 0 % error rate (ER) of the proposed method versus 0.64± ~ 0 % of ER between human experts (~1% difference)).
Christopher M Cheatum - One of the best experts on this subject based on the ideXlab platform.
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Edge Pixel referencing suppresses correlated baseline noise in heterodyned spectroscopies
Journal of Chemical Physics, 2020Co-Authors: Kevin Robben, Christopher M CheatumAbstract:Referencing schemes are commonly used in heterodyned spectroscopies to mitigate correlated baseline noise arising from shot-to-shot fluctuations of the local oscillator. Although successful, these methods rely on careful Pixel-to-Pixel matching between the two spectrographs. A recent scheme introduced by Feng et al. [Opt. Express 27(15), 20323–20346 (2019)] employed a correlation matrix to allow free mapping between dissimilar spectrographs, leading to the first demonstration of floor noise limited detection on a multichannel array used in heterodyned spectroscopy. In addition to their primary results using a second reference spectrometer, Feng et al. briefly demonstrated the flexibility of their method by referencing to same-array Pixels at the two spectral Edges (i.e., Edge-Pixel referencing). We present a comprehensive study of this approach, which we term Edge-Pixel referencing, including optimization of the approach, assessment of the performance, and determination of the effects of background responses. We show that, within some limitations, the distortions due to background signals will not affect the 2D IR line shape or amplitude and can be mitigated by band narrowing of the pump beams. We also show that the performance of Edge-Pixel referencing is comparable to that of referencing to a second spectrometer in terms of noise suppression and that the line shapes and amplitudes of the spectral features are, within the measurement error, identical. Altogether, these results demonstrate that Edge-Pixel referencing is a powerful approach for noise suppression in heterodyned spectroscopies, which requires no new hardware and, so, can be implemented as a software solution for anyone performing heterodyned spectroscopy with multichannel array detectors already.
