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X-ray spectral imaging provides quantitative imaging of trace elements inbiological sample with high sensitivity. We propose a novel algorithm topromote the signal-to-noise ratio (SNR) of X-ray spectral images that have lowphoton counts. Firstly, we estimate the image data area that belongs to thehomogeneous parts through confidence interval testing. Then, we apply thePoisson regression through its maximum likelihood estimation on this area toestimate the true photon counts from the Poisson noise corrupted data. Unlikeother denoising methods based on regression analysis, we use the bootstrapresampling method to ensure the accuracy of regression estimation. Finally, weuse a robust local nonparametric regression method to estimate the baseline andsubsequently subtract it from the X-ray spectral data to further improve theSNR of the data. Experiments on several real samples show that the proposedmethod performs better than some state-of-the-art approaches to ensure accuracyand precision for quantitative analysis of the different trace elements instandard reference biological sample.

Reducing Poisson noise and baseline drift in X-ray spectral images with bootstrap Poisson regression and robust nonparametric regression