- 标题
- 摘要
- 关键词
- 实验方案
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Fast sampling from Wiener posteriors for image data with dataflow engines
摘要： We use Dataflow Engines (DFE) to construct an efficient Wiener filter of noisy and incomplete image data, and to quickly draw probabilistic samples of the compatible true underlying images from the Wiener posterior. Dataflow computing is a powerful approach using reconfigurable hardware, which can be deeply pipelined and is intrinsically parallel. The unique Wiener-filtered image is the minimum-variance linear estimate of the true image (if the signal and noise covariances are known) and the most probable true image (if the signal and noise are Gaussian distributed). However, many images are compatible with the data with different probabilities, given by the analytic posterior probability distribution referred to as the Wiener posterior. The DFE code also draws large numbers of samples of true images from this posterior, which allows for further statistical analysis. Naive computation of the Wiener-filtered image is impractical for large datasets, as it scales as n3, where n is the number of pixels. We use a messenger field algorithm, which is well suited to a DFE implementation, to draw samples from the Wiener posterior, that is, with the correct probability we draw samples of noiseless images that are compatible with the observed noisy image. The Wiener-filtered image can be obtained by a trivial modification of the algorithm. We demonstrate a lower bound on the speed-up, from drawing 105 samples of a 1282 image, of 11.3 ± 0.8 with 8 DFEs in a 1U MPC-X box when compared with a 1U server presenting 32 CPU threads. We also discuss a potential application in astronomy, to provide better dark matter maps and improved determination of the parameters of the Universe.
关键词： Reconfigurable hardware,MCMC,Data analysis,Wiener filter,Bayesian statistics,Dataflow engines
更新于2025-09-23 15:22:29
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[IEEE 2019 Chinese Control And Decision Conference (CCDC) - Nanchang, China (2019.6.3-2019.6.5)] 2019 Chinese Control And Decision Conference (CCDC) - Correlations between generation output of two different photovoltaic power stationsa??a??based on the field data of Xinjiang
摘要： This paper presents a Bayesian algorithm for linear spectral unmixing of hyperspectral images that accounts for anomalies present in the data. The model proposed assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional nonlinear term modeling anomalies, and additive Gaussian noise. A Markov random field is used for anomaly detection based on the spatial and spectral structures of the anomalies. This allows outliers to be identified in particular regions and wavelengths of the data cube. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint linear unmixing and anomaly detection algorithm. Simulations conducted with synthetic and real hyperspectral images demonstrate the accuracy of the proposed unmixing and outlier detection strategy for the analysis of hyperspectral images.
关键词： unsupervised spectral unmixing,Hyperspectral imagery,MCMC,Bayesian estimation,anomaly detection
更新于2025-09-19 17:13:59
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Olive Biophysical Property Estimation Based on Sentinel-2 Image Inversion
摘要： In this paper, we study the estimation of olive tree biophysical properties driven by Sentinel-2 (S2) image inversion. The latter is based on the forward/backward radiative transfer (RT) model. The forward step is done simulating DART on a realistic tree mock-up, whereas the backward is done based on a coupling between the LookUP Table (LUT) and the Markov Chain Monte Carlo (MCMC). The parameters Leaf area index (LAI), chlorophyll (Cab) water (Cw) contents and mesophyll structure (N) are derived. The results are promising, in particular LAI and Cab values are close to those found in litterature.
关键词： olives biophysical properties,Sentinel-2,DART,MCMC,Planet
更新于2025-09-10 09:29:36