Calibration of the Marshall Grazing Incidence X-Ray Spectrometer Experiment. II. Flight Instrument Calibration

The Marshall Grazing Incidence X-ray Spectrometer (MaGIXS) is a sounding rocket experiment that observes the soft X-ray spectrum of the Sun from 6.0–24 Å (0.5–2.0 keV), successfully launched on 2021 July 30. End-to-end alignment of the flight instrument and calibration experiments are carried out using the X-ray and Cryogenic Facility at NASA Marshall Space Flight Center. In this paper, we present the calibration experiments of MaGIXS, which include wavelength calibration, measurement of line spread function, and determination of effective area. Finally, we use the measured instrument response function to predict the expected count rates for MaGIXS flight observation looking at a typical solar active region.

Prediction of the layered ink layout for 3D printers considering a desired skin color and line spread function

We propose a layout estimation method for multi-layered ink using a measurement of the line spread function (LSF) and machine learning. The three-dimensional printing market for general consumers focuses on the reproduction of realistic appearance. In particular, for the reproduction of human skin, it is important to control translucency by adopting a multilayer structure. Traditionally, layer design has depended on the experience of designers. We, therefore, developed an efficient layout estimation to provide arbitrary skin color and translucency. In our method, we create multi-layered color patches of human skin and measure the LSF as a metric of translucency, and we employ a neural network trained with the data to estimate the layout. As an evaluation, we measured the LSF from the computer-graphics-created skin and fabricate skin using the estimated layout; evaluation with root-mean-square error showed that we can obtain color and translucency that are close to the target.

Towards Streamlined Single-Image Super-Resolution: Demonstration with 10 m Sentinel-2 Colour and 10–60 m Multi-Spectral VNIR and SWIR Bands

Higher spatial resolution imaging data are considered desirable in many Earth observation applications. In this work, we propose and demonstrate the TARSGAN (learning Terrestrial image deblurring using Adaptive weighted dense Residual Super-resolution Generative Adversarial Network) system for Super-resolution Restoration (SRR) of 10 m/pixel Sentinel-2 “true” colour images as well as all the other multispectral bands. In parallel, the ELF (automated image Edge detection and measurements of edge spread function, Line spread function, and Full width at half maximum) system is proposed to achieve automated and precise assessments of the effective resolutions of the input and SRR images. Subsequent ELF measurements of the TARSGAN SRR results suggest an averaged effective resolution enhancement factor of about 2.91 times (equivalent to ~3.44 m/pixel for the 10 m/pixel bands) given a nominal SRR upscaling factor of 4 times. Several examples are provided for different types of scenes from urban landscapes to agricultural scenes and sea-ice floes.

Layout Estimation for Layered Ink of 3D Printer to Reproduce the Desired Line Spread Function of Skin using Simulated Data

In this article, the authors propose a method to estimate the ink layer layout for a three-dimensional (3D) printer. This enables 3D printed skin to be produced with the desired translucency, which they represent as line spread function (LSF). A deep neural network in an encoder–decoder model is used for the estimation. It was previously reported that machine learning is an effective way to formulate the complex relationship between optical properties such as LSF and the ink layer layout in a 3D printer. However, although 3D printers are more widespread, the printing process is still time-consuming. Hence, it may be difficult to collect enough data to train a neural network sufficiently. Therefore, in this research, they prepare the training data, which is the correspondence between an LSF and the ink layer layout in a 3D printer, via computer simulation. They use a method to simulate the subsurface scattering of light for multilayered media. The deep neural network was trained with the simulated data and evaluated using a CG skin object. The result shows that their proposed method can estimate an appropriate ink layer layout that closely reproduces the target color and translucency.