The Mapping Imaging Spectrometer for Europa (MISE).

The Mapping Imaging Spectrometer for Europa (MISE) is an infrared compositional instrument that will fly on NASA's Europa Clipper mission to the Jupiter system. MISE is designed to meet the Level-1 science requirements related to the mission's composition science objective to "understand the habitability of Europa's ocean through composition and chemistry" and to contribute to the geology science and ice shell and ocean objectives, thereby helping Europa Clipper achieve its mission goal to "explore Europa to investigate its habitability." MISE has a mass of 65 kg and uses an energy per flyby of 75.2 W-h. MISE will detect illumination from 0.8 to 5 µm with 10 nm spectral resolution, a spatial sampling of 25 m per pixel at 100 km altitude, and 300 cross-track pixels, enabling discrimination among the two principal states of water ice on Europa, identification of the main non-ice components of interest: salts, acids, and organics, and detection of trace materials as well as some thermal signatures. Furthermore, the spatial resolution and global coverage that MISE will achieve will be complemented by the higher spectral resolution of some Earth-based assets. MISE, combined with observations collected by the rest of the Europa Clipper payload, will enable significant advances in our understanding of how the large-scale structure of Europa's surface is shaped by geological processes and inform our understanding of the surface at microscale. This paper describes the planned MISE science investigations, instrument design, concept of operations, and data products.

Atmospheric Lengthscales for Global VSWIR Imaging Spectroscopy.

Future global Visible Shortwave Infrared Imaging Spectrometers, such as the Surface Biology and Geology (SBG) mission, will regularly cover the Earth's entire terrestrial land area. These missions need high fidelity atmospheric correction to produce consistent maps of terrestrial and aquatic ecosystem traits. However, estimation of surface reflectance and atmospheric state is computationally challenging, and the terabyte data volumes of global missions will exceed available processing capacity. This article describes how missions can overcome this bottleneck using the spatial continuity of atmospheric fields. Contemporary imaging spectrometers oversample atmospheric spatial variability, so it is not necessary to invert every pixel. Spatially sparse solutions can train local linear emulators that provide fast, exact inversions in their vicinity. We find that estimating the atmosphere at 200 m scales can outperform traditional atmospheric correction, improving speed by one to two orders of magnitude with no measurable penalty to accuracy. We validate performance with an airborne field campaign, showing reflectance accuracies with RMSE of 1.1% or better compared to ground measurements of diverse targets. These errors are statistically consistent with retrieval uncertainty budgets. Local emulators can close the efficiency gap and make rigorous model inversion algorithms feasible for global missions such as SBG.

Self-calibrated 3D differential phase contrast microscopy with optimized illumination.

3D phase imaging recovers an object's volumetric refractive index from intensity and/or holographic measurements. Partially coherent methods, such as illumination-based differential phase contrast (DPC), are particularly simple to implement in a commercial brightfield microscope. 3D DPC acquires images at multiple focus positions and with different illumination source patterns in order to reconstruct 3D refractive index. Here, we present a practical extension of the 3D DPC method that does not require a precise motion stage for scanning the focus and uses optimized illumination patterns for improved performance. The user scans the focus by hand, using the microscope's focus knob, and the algorithm self-calibrates the axial position to solve for the 3D refractive index of the sample through a computational inverse problem. We further show that the illumination patterns can be optimized by an end-to-end learning procedure. Combining these two, we demonstrate improved 3D DPC with a commercial microscope whose only hardware modification is LED array illumination.

Low-cost, sub-micron resolution, wide-field computational microscopy using opensource hardware.

The revolution in low-cost consumer photography and computation provides fertile opportunity for a disruptive reduction in the cost of biomedical imaging. Conventional approaches to low-cost microscopy are fundamentally restricted, however, to modest field of view (FOV) and/or resolution. We report a low-cost microscopy technique, implemented with a Raspberry Pi single-board computer and color camera combined with Fourier ptychography (FP), to computationally construct 25-megapixel images with sub-micron resolution. New image-construction techniques were developed to enable the use of the low-cost Bayer color sensor, to compensate for the highly aberrated re-used camera lens and to compensate for misalignments associated with the 3D-printed microscope structure. This high ratio of performance to cost is of particular interest to high-throughput microscopy applications, ranging from drug discovery and digital pathology to health screening in low-income countries. 3D models and assembly instructions of our microscope are made available for open source use.

High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images.

Optical diffraction tomography (ODT) reconstructs a sample's volumetric refractive index (RI) to create high-contrast, quantitative 3D visualizations of biological samples. However, standard implementations of ODT use interferometric systems, and so are sensitive to phase instabilities, complex mechanical design, and coherent noise. Furthermore, their reconstruction framework is typically limited to weakly scattering samples, and thus excludes a whole class of multiple-scattering samples. Here, we implement a new 3D RI microscopy technique that utilizes a computational multi-slice beam propagation method to invert the optical scattering process and reconstruct high-resolution (NA > 1.0) 3D RI distributions of multiple-scattering samples. The method acquires intensity-only measurements from different illumination angles and then solves a nonlinear optimization problem to recover the sample's 3D RI distribution. We experimentally demonstrate the reconstruction of samples with varying amounts of multiple-scattering: a 3T3 fibroblast cell, a cluster of C. elegans embryos, and a whole C. elegans worm, with lateral and axial resolutions of ≤ 240 nm and ≤ 900 nm, respectively. The results of this work lays groundwork for future studies into using optical wavelengths to probe 3D RI distributions of highly scattering biological organisms.

Efficient illumination angle self-calibration in Fourier ptychography.

Fourier ptychography captures intensity images with varying source patterns (illumination angles) in order to computationally reconstruct large space-bandwidth-product images. Accurate knowledge of the illumination angles is necessary for good image quality; hence, calibration methods are crucial, despite often being impractical or slow. Here, we propose a fast, robust, and accurate self-calibration algorithm that uses only experimentally collected data and general knowledge of the illumination setup. First, our algorithm makes a fast direct estimate of the brightfield illumination angles based on image processing. Then, a more computationally intensive spectral correlation method is used inside the iterative solver to further refine the angle estimates of both brightfield and darkfield images. We demonstrate our method for correcting large and small misalignment artifacts in 2D and 3D Fourier ptychography with different source types: an LED array, a galvo-steered laser, and a high-NA quasi-dome LED illuminator.

UC Berkeley Sculpted Light in the Brain 2017 debates future technologies to communicate with the brain.

This article summarizes presentations at Sculpted Light in the Brain 2017.