A Variable Density Sampling Scheme for Compressive Fourier Transform Interferometry

Fourier Transform Interferometry (FTI) is an appealing Hyperspectral (HS) imaging modality for many applications demanding high spectral resolution, e.g., in fluorescence microscopy. However, the effective resolution of FTI is limited by the durability of biological elements when exposed to illuminating light. Overexposed elements are subject to photo-bleaching and become unable to fluoresce. In this context, the acquisition of biological HS volumes based on sampling the Optical Path Difference (OPD) axis at Nyquist rate leads to unpleasant trade-offs between spectral resolution, quality of the HS volume, and light exposure intensity. We propose two variants of the FTI imager, i.e., Coded Illumination-FTI (CI-FTI) and Structured Illumination FTI (SI-FTI), based on the theory of compressive sensing (CS). These schemes efficiently modulate light exposure temporally (in CI-FTI) or spatiotemporally (in SI-FTI). Leveraging a variable density sampling strategy recently introduced in CS, we provide near-optimal illumination strategies, so that the light exposure imposed on a biological specimen is minimized while the spectral resolution is preserved. Our analysis focuses on two criteria: (i) a trade-off between exposure intensity and the quality of the reconstructed HS volume for a given spectral resolution; (ii) maximizing HS volume quality for a fixed spectral resolution and constrained exposure budget. Our contributions can be adapted to an FTI imager without hardware modifications. The reconstruction of HS volumes from CS-FTI measurements relies on an $l_1$-norm minimization problem promoting a spatiospectral sparsity prior. Numerically, we support the proposed methods on synthetic data and simulated CS measurements (from actual FTI measurements) under various scenarios. In particular, the biological HS volumes can be reconstructed with a three-to-ten-fold reduction in the light exposure.Comment: 45 pages, 11 figure

The advantages of sub-sampling and Inpainting for scanning transmission electron microscopy

Images and spectra obtained from aberration corrected scanning transmission electron microscopes (STEM) are now used routinely to quantify the morphology, structure, composition, chemistry, bonding, and optical/electronic properties of nanostructures, interfaces, and defects in many materials/biological systems. However, obtaining quantitative and reproducible atomic resolution observations from some experiments is actually harder with these ground-breaking instrumental capabilities, as the increase in beam current from using the correctors brings with it the potential for electron beam modification of the specimen during image acquisition. This beam effect is even more acute for in situ STEM observations, where the desired outcome being investigated is a result of a series of complicated transients, all of which can be modified in unknown ways by the electron beam. The aim in developing and applying new methods in STEM is, therefore, to focus on more efficient use of the dose that is supplied to the sample and to extract the most information from each image (or set of images). For STEM (and for that matter, all electron/ion/photon scanning systems), one way to achieve this is by sub-sampling the image and using Inpainting algorithms to reconstruct it. By separating final image quality from overall dose in this way and manipulating the dose distribution to be best for the stability of the sample, images can be acquired both faster and with less beam effects. In this paper, the methodology behind sub-sampling and Inpainting is described, and the potential for Inpainting to be applied to novel real time dynamic experiments will be discussed

A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy

Cryo Focused Ion-Beam Scanning Electron Microscopy (cryo FIB-SEM) enables three-dimensional and nanoscale imaging of biological specimens via a slice and view mechanism. The FIB-SEM experiments are, however, limited by a slow (typically, several hours) acquisition process and the high electron doses imposed on the beam sensitive specimen can cause damage. In this work, we present a compressive sensing variant of cryo FIB-SEM capable of reducing the operational electron dose and increasing speed. We propose two Targeted Sampling (TS) strategies that leverage the reconstructed image of the previous sample layer as a prior for designing the next subsampling mask. Our image recovery is based on a blind Bayesian dictionary learning approach, i.e., Beta Process Factor Analysis (BPFA). This method is experimentally viable due to our ultra-fast GPU-based implementation of BPFA. Simulations on artificial compressive FIB-SEM measurements validate the success of proposed methods: the operational electron dose can be reduced by up to 20 times. These methods have large implications for the cryo FIB-SEM community, in which the imaging of beam sensitive biological materials without beam damage is crucial.Comment: Submitted to ICASSP 202

The Potential of Subsampling and Inpainting for Fast Low-Dose Cryo FIB-SEM Imaging and Tomography

Traditional image acquisition for cryo focused ion-beam scanning electron microscopy tomography often sees thousands of images being captured over a period of many hours, with immense data sets being produced. When imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both of these aspects, allowing fast and low-dose imaging to take place in the FIB-SEM without an appreciable low in image quality. In this work, experimental data is presented which validates subsampling and inpainting as a useful tool for convenient and reliable data acquisition in a FIB-SEM, with new methods of handling 3-dimensional data being employed in context of dictionary learning and inpainting algorithms using a newly developed microscope control software and data recovery algorithm.Comment: In submission to "Microscopy and Microanalysis" journal. Authorship reviewed from previous submissio

A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy

Cryo Focused Ion-Beam Scanning Electron Microscopy (cryo FIB-SEM) enables three-dimensional and nanoscale imaging of biological specimens via a slice and view mechanism. The FIB-SEM experiments are, however, limited by a slow (typically, several hours) acquisition process and the high electron doses imposed on the beam sensitive specimen can cause damage. In this work, we present a compressive sensing variant of cryo FIB-SEM capable of reducing the operational electron dose and increasing speed. We propose two Targeted Sampling (TS) strategies that leverage the reconstructed image of the previous sample layer as a prior for designing the next subsampling mask. Our image recovery is based on a blind Bayesian dictionary learning approach, i.e., Beta Process Factor Analysis (BPFA). This method is experimentally viable due to our ultra-fast GPU-based implementation of BPFA. Simulations on artificial compressive FIB-SEM measurements validate the success of proposed methods: the operational electron dose can be reduced by up to 20 times. These methods have large implications for the cryo FIB-SEM community, in which the imaging of beam sensitive biological materials without beam damage is crucial

A Targeted Sampling Strategy for Compressive Cryo Focused Ion Beam Scanning Electron Microscopy

Cryo Focused Ion-Beam Scanning Electron Microscopy (cryo FIB-SEM) enables three-dimensional and nanoscale imaging of biological specimens via a slice and view mechanism. The FIB-SEM experiments are, however, limited by a slow (typically, several hours) acquisition process and the high electron doses imposed on the beam sensitive specimen can cause damage. In this work, we present a compressive sensing variant of cryo FIB-SEM capable of reducing the operational electron dose and increasing speed. We propose two Targeted Sampling (TS) strategies that leverage the reconstructed image of the previous sample layer as a prior for designing the next subsampling mask. Our image recovery is based on a blind Bayesian dictionary learning approach, i.e., Beta Process Factor Analysis (BPFA). This method is experimentally viable due to our GPU-based implementation of BPFA. Simulations on artificial compressive FIB-SEM measurements validate the success of proposed methods: the operational electron dose can be reduced by up to 20 times. These methods have large implications for the cryo FIB-SEM community, in which the imaging of beam sensitive biological materials without beam damage is crucial