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Low light image enhancement is an important challenge for the development of robust computer vision algorithms. The machine learning approaches to this have been either unsupervised, supervised based on paired dataset or supervised based on…
Deep neural networks (DNNs) have recently become the leading method for low-light image enhancement (LLIE). However, despite significant progress, their outputs may still exhibit issues such as amplified noise, incorrect white balance, or…
This paper presents an efficient optimization technique for gridless {2-D} line spectrum estimation, named decoupled atomic norm minimization (D-ANM). The framework of atomic norm minimization (ANM) is considered, which has been…
We present a new approach (MADE) that generates mass, age, and distance estimates of red giant stars from a combination of astrometric, photometric, and spectroscopic data. The core of the approach is a Bayesian artificial neural network…
The automated identification of extragalactic objects in large surveys provides reliable and reproducible samples of galaxies in less time than procedures involving human interaction. However, regions near the Galactic disc are more…
We present a novel method of classifying Type Ia supernovae using convolutional neural networks, a neural network framework typically used for image recognition. Our model is trained on photometric information only, eliminating the need for…
Breast cancer is one of the deadliest cancer worldwide. Timely detection could reduce mortality rates. In the clinical routine, classifying benign and malignant tumors from ultrasound (US) imaging is a crucial but challenging task. An…
Interstellar molecules, which play an important role in astrochemistry, are identified using observed spectral lines. Despite the advent of spectral analysis tools in the past decade, the identification of spectral lines remains a tedious…
Several ongoing and upcoming radio telescopes aim to detect either the global 21 cm signal or the 21 cm power spectrum. The extragalactic radio background, as detected by ARCADE-2 and LWA-1, suggests a strong radio background from cosmic…
Aims: We introduce a new deep-learning approach for the reconstruction of 3D dust density and temperature distributions from multi-wavelength dust emission observations on the scale of individual star-forming cloud cores (<0.2pc). Methods:…
We develop and evaluate a deep learning algorithm to classify multiple catheters on neonatal chest and abdominal radiographs. A convolutional neural network (CNN) was trained using a dataset of 777 neonatal chest and abdominal radiographs,…
We present an Artificial Neural Network (ANN) model of photospheric lithium depletion in cool stars (3000 < Teff / K < 6500), producing estimates and probability distributions of age from Li I 6708A equivalent width (LiEW) and effective…
With the increasing use of nonlinear devices in both generation and consumption of power, it is essential that we develop accurate and quick control for active filters to suppress harmonics. Time delays between input and output are…
Interstellar dust extinction law is essential for interpreting observations. In this work, we investigate the ultraviolet (UV)--mid-infrared (IR) extinction law of the Taurus molecular cloud and its possible variations. We select 504,988…
Time-domain astronomy is progressing rapidly with the ongoing and upcoming large-scale photometric sky surveys led by the Vera C. Rubin Observatory project (LSST). Billions of variable sources call for better automatic classification…
Unmanned Aerial Vehicles (UAVs), equipped with camera sensors can facilitate enhanced situational awareness for many emergency response and disaster management applications since they are capable of operating in remote and difficult to…
Recently, audio-visual scene classification (AVSC) has attracted increasing attention from multidisciplinary communities. Previous studies tended to adopt a pipeline training strategy, which uses well-trained visual and acoustic encoders to…
Stellar spectral interpolation is critical technique employed by fitting software to derive the physical parameters of stars. This approach is necessary because on-the-go generation of synthetic stellar spectra is not possible due to the…
Interstellar extinction in ultraviolet is the most severe in comparison with optical and infrared wavebands and a precise determination plays an important role in correctly recovering the ultraviolet brightness and colors of objects. By…
We present the measurement of the line-of-sight extinction of the dusty torus for a large number of obscured active galactic nuclei (AGNs) based on the reddening of the colour of the variable flux component in near-infrared (NIR)…