Advanced denoising for X-ray ptychography

Abstract

The success of ptychographic imaging experiments strongly depends on achieving high signal-to-noise ratio. This is particularly important in nanoscale imaging experiments when diffraction signals are very weak and the experiments are accompanied by significant parasitic scattering (background), outliers or correlated noise sources. It is also critical when rare events such as cosmic rays, or bad frames caused by electronic glitches or shutter timing malfunction take place. In this paper, we propose a novel iterative algorithm with rigorous analysis that exploits the direct forward model for parasitic noise and sample smoothness to achieve a thorough characterization and removal of structured and random noise. We present a formal description of the proposed algorithm and prove its convergence under mild conditions. Numerical experiments from simulations and real data (both soft and hard X-ray beamlines) demonstrate that the proposed algorithms produce better results when compared to state-of-the-art methods.

Improved ptychographic inspection of EUV reticles via inclusion of prior information

“…TV is commonly employed due to its effectiveness in smoothing noise -by favouring images that have a sparse gradient -while preserving edges. In recent years there have been a number of studies reporting implementations of TV regularized ptychography [20][21][22]. In this work we have taken a different approach and instead of considering a regularizer that promotes generic properties of the reconstructed image (like its sparsity or the sparsity of its gradient), the regularizer R α (O) has been designed to promote adhesion to a given prior image (object):…”

“…Recent works have reported the use of total variation as regularizer for denoising in ptychography [20][21][22]. Here we compare results obtained using the two different regularizers R α (O j ) = α||O j (r) − O p, j (r)|| 2 and R α T V (O j ) = α TV ||∇O j (r)|| 1 .…”

“…In the context of CDI, prior information about the amplitude of the ptychographic illumination function enabled subwavelength imaging at the edges of the lines of a periodic object [19]. Recent studies have further shown the benefits that stem from the inclusion of the total variation (TV) prior in the ptychographic algorithms [20][21][22].…”

Ptychographic Nano-Analytical Microscope (PtyNAMi) at PETRA III: signal-to-background optimization for imaging with high sensitivity

“…In addition, the background is misinterpreted as coherent diffraction signal unless it is properly modelled and considered in the ptychographic reconstruction. 38,39 In any case, the reconstruction suffers as compared to one from low-background data. 3 In order to achieve highest sensitivity and resolution, it is thus favorable to reduce background scattering as much as possible in a ptychographic scanning microscope.…”

A Monte Carlo ray-tracing simulation of coherent X-ray diffractive imaging

“…Nonideal conditions, such as partial coherence, mechanical instabilities and electronic noise, all contribute to the degradation of resolution by decreasing data quality. Although these can be accounted for in the reconstruction algorithms, and several strategies to mitigate their detrimental effects have been proposed in the literature (Thibault & Guizar-Sicairos, 2012;Godard et al, 2012;Chang et al, 2019), they still represent a strong limitation for the ultimate achievable resolution. This is demonstrated by the fact that, although remarkable for a non-destructive technique, the current ISSN 1600-5775 # 2020 International Union of Crystallography record resolutions for tomographic reconstructions based on full-field CDI [$ 10 nm (Takahashi et al, 2010)] or ptychographic projections [14.6 nm (Holler et al, 2017), 13.6 nm (Ramos et al, 2017)] are probably an order of magnitude higher than the theoretical limit (Marchesini et al, 2003;Dietze & Shpyrko, 2015), at least for inorganic materials (Howells et al, 2009).…”