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Recent advancements in lightweight neural networks have significantly improved the efficiency of deploying deep learning models on edge hardware. However, most existing architectures still trade accuracy for latency, which limits their…

We provide an analysis of a convolutional neural network's ability to identify the lensing signal of single dark matter subhalos in strong galaxy-galaxy lenses in the presence of increasingly complex source light morphology. We simulate a…

Object detection in natural scenes can be a challenging task. In many real-life situations, the visible spectrum is not suitable for traditional computer vision tasks. Moving outside the visible spectrum range, such as the thermal spectrum…

Object detection is one of the fundamental objectives in Applied Computer Vision. In some of the applications, object detection becomes very challenging such as in the case of satellite image processing. Satellite image processing has…

We aim to obtain a complete sample of redshift > 3.6 radio QSOs from FIRST sources having star-like counterparts in the SDSS DR5 photometric survey (r<=20.2). We found that simple supervised neural networks, trained on sources with SDSS…

We introduce a novel galaxy classification methodology based on the visible spectra of a sample of over 68,000 nearby ($z\leq 0.1$) Sloan Digital Sky Survey lenticular (S0) galaxies. Unlike traditional diagnostic diagrams, which rely on a…

The China Space Station Telescope (CSST) is a forthcoming space-based optical telescope designed to co-orbit with the Chinese Space Station. With a planned slitless spectroscopic survey spanning a broad wavelength range of $255-1000$nm and…

Wide-field slitless grism spectroscopy is difficult in the co-spatial regime, where multiple sources share one cross-dispersion PSF element and catalogue-level decontamination (aXe/LINEAR/Grizli) does not apply -- the regime of strong…

Despite the breakthrough deep learning performances achieved for automatic object detection, small target detection is still a challenging problem, especially when looking at fast and accurate solutions suitable for mobile or edge…

The Grism Lens-Amplified Survey from Space (GLASS) is a Hubble Space Telescope (HST) Large Program, which will obtain 140 orbits of grism spectroscopy of the core and infall regions of 10 galaxy clusters, selected to be among the very best…

The next generation of wide spectroscopic surveys such as Euclid will scan the sky in the near-infrared to obtain both photometry and spectroscopy. For this purpose, the Euclid telescope will rely on a Near-Infrared Spectrometer and…

The Chinese Space Station Telescope (CSST) spectroscopic survey aims to deliver high-quality low-resolution ($R > 200$) slitless spectra for hundreds of millions of targets down to a limiting magnitude of about 21 mag, distributed within a…

We describe a new approach to obtaining very high surface densities of optical spectra in astronomical observations with extremely accurate subtraction of night sky emission. The observing technique requires that the telescope is nodded…

Object detection and classification are crucial tasks across various application domains, particularly in the development of safe and reliable Advanced Driver Assistance Systems (ADAS). Existing deep learning-based methods such as…

A method for spatial deconvolution of spectra is presented. It follows the same fundamental principles as the ``MCS image deconvolution algorithm'' (Magain, Courbin, Sohy, 1998) and uses information contained in the spectrum of a reference…

Slitless spectrometers can provide simultaneous imaging and spectral data over an extended field of view, thereby allowing rapid data acquisition for extended sources. In some instances, when the object is greatly extended or the spectral…

We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…

Imaging through scattering media is encountered in many disciplines or sciences, ranging from biology, mesescopic physics and astronomy. But it is still a big challenge because light suffers from multiple scattering is such media and can be…

Multispectral imaging and deep learning have emerged as powerful tools supporting diverse use cases from autonomous vehicles, to agriculture, infrastructure monitoring and environmental assessment. The combination of these technologies has…

Scattering obscures information carried by wave by producing a speckle pattern, posing a common challenge across various fields, including microscopy and astronomy. Traditional methods for extracting information from speckles often rely on…