相关论文: Compression Method for Solar Polarization Spectra …
Galaxy model subtraction removes the smooth light of nearby galaxies so that fainter sources (e.g., stars, star clusters, background galaxies) can be identified and measured. Traditional approaches (isophotal or parametric fitting) are…
High resolution galaxy spectra contain much information about galactic physics, but the high dimensionality of these spectra makes it difficult to fully utilize the information they contain. We apply variational autoencoders (VAEs), a…
We present the results from first spectropolarimetric observations of the solar photosphere acquired at the Dunn Solar Telescope with the Interferometric Bidimensional Spectrometer. Full Stokes profiles were measured in the Fe I 630.15 nm…
Multiline techniques assuming similar line profiles have become a standard tool in stellar astronomy for increasing the signal-to-noise ratio (SNR) of spectropolarimetric measurements. However, due to the widely-used weak field…
Advancements in optical quantum technologies have been enabled by the generation, manipulation, and characterization of light, with identification based on its photon statistics. However, characterizing light and its sources through single…
Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual features are most salient. While recent work from our group and…
Learning rich data representations from unlabeled data is a key challenge towards applying deep learning algorithms in downstream tasks. Several variants of variational autoencoders (VAEs) have been proposed to learn compact data…
We explore the use of deep neural networks for nonlinear dimensionality reduction in climate applications. We train convolutional autoencoders (CAEs) to encode two temperature field datasets from pre-industrial control runs in the CMIP5…
High-fidelity compression of multispectral solar imagery remains challenging for space missions, where limited bandwidth must be balanced against preserving fine spectral and spatial details. We present a learned image compression framework…
Is there really much more to say about sparse autoencoders (SAEs)? Autoencoders in general, and SAEs in particular, represent deep architectures that are capable of modeling low-dimensional latent structure in data. Such structure could…
The volume of data being collected in solar physics has exponentially increased over the past decade and with the introduction of the $\textit{Daniel K. Inouye Solar Telescope}$ (DKIST) we will be entering the age of petabyte solar data.…
We analyze spectropolarimetric data taken with the Hinode spacecraft in quiet solar regions at the disk center. Distorted redshifted Stokes $V$ profiles are found showing a characteristic evolution that always follows the same sequence of…
We present the first classification of SOT/SP circular polarization measurements with the aim of highlighting exhaustively the whole variety of Stokes V shapes emerging from the quiet Sun. k-means is used to classify HINODE SOT/SP Stokes V…
When interpreting spectropolarimetric observations of the solar atmosphere, wavelength variations of the emergent intensity and polarization translate into information on the depth stratification of physical parameters. We aim to quantify…
With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (e.g.…
Deep learning has made significant strides in medical imaging, leveraging the use of large datasets to improve diagnostics and prognostics. However, large datasets often come with inherent errors through subject selection and acquisition.…
Image compression has been investigated as a fundamental research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and is gradually being used in image compression. In this paper, we…
We study the structure of the photospheric magnetic field of the quiet Sun by investigating weak spectro-polarimetric signals. We took a sequence of Stokes spectra of the Fe I 630.15 nm and 630.25 nm lines in a region of quiet Sun near the…
Hyperspectral data acquired through remote sensing are invaluable for environmental and resource studies. While rich in spectral information, various complexities such as environmental conditions, material properties, and sensor…
This paper presents a novel convolutional neural network (CNN) based image compression framework via scalable auto-encoder (SAE). Specifically, our SAE based deep image codec consists of hierarchical coding layers, each of which is an…