Poisson image denoising by piecewise principal component analysis and its application in single‐particle X‐ray diffraction imaging

Abstract

This study describes an improved method for Poisson image denoising that is based on a state-of-the-art Poisson denoising approach known as non-local principal component analysis (NLPCA). The new method is referred to as PieceWise Principal Component Analysis (PWPCA). In PWPCA, the given image is first split into pieces, then NLPCA is run on each image piece, and finally the entire image is reconstituted by a weighted combination of the NLPCA-processed image pieces. Using standard test images with Poisson noise, the authors show that PWPCA restores images more effectively than state-of-the-art Poisson denoising approaches. In addition, and to the best of their knowledge, they show the first application of such approaches to single-particle X-ray free-electron laser (XFEL) data. They show that the resolution of three-dimensional reconstruction from XFEL diffraction images is improved when the data are preprocessed with PWPCA. XFELs are currently under rapid development to allow high-resolution biomolecular structure determination at near-physiological conditions. Data analysis methods developments follow these technological advances and are expected to have high impact in structural biology and drug design. This study contributes to these developments. As little experimental single-particle XFEL data is available still, the XFEL experiments shown here were performed with simulated data.