  X-ray spectral imaging provides quantitative imaging of trace elements in
biological sample with high sensitivity. We propose a novel algorithm to
promote the signal-to-noise ratio (SNR) of X-ray spectral images that have low
photon counts. Firstly, we estimate the image data area that belongs to the
homogeneous parts through confidence interval testing. Then, we apply the
Poisson regression through its maximum likelihood estimation on this area to
estimate the true photon counts from the Poisson noise corrupted data. Unlike
other denoising methods based on regression analysis, we use the bootstrap
resampling method to ensure the accuracy of regression estimation. Finally, we
use a robust local nonparametric regression method to estimate the baseline and
subsequently subtract it from the X-ray spectral data to further improve the
SNR of the data. Experiments on several real samples show that the proposed
method performs better than some state-of-the-art approaches to ensure accuracy
and precision for quantitative analysis of the different trace elements in
standard reference biological sample.
