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Deep neural networks have proven to be very effective for computer vision tasks, such as image classification, object detection, and semantic segmentation -- these are primarily applied to color imagery and video. In recent years, there has…

We present a blind multiframe image-deconvolution method based on robust statistics. The usual shortcomings of iterative optimization of the likelihood function are alleviated by minimizing the M-scale of the residuals, which achieves more…

Next generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Machine learning methods are increasingly becoming the most…

Strong Lensing is a powerful probe of the matter distribution in galaxies and clusters and a relevant tool for cosmography. Analyses of strong gravitational lenses with Deep Learning have become a popular approach due to these astronomical…

Image denoising based on deep learning has witnessed significant advancements in recent years. However, existing deep learning methods lack quantitative control of the deviation or error on denoised images. The neural networks Self2Self is…

Radio astronomy is facing critical challenges due to an ever-increasing human-made signal density filling up the radio spectrum. With the rise of satellites, mobile networks, and other wireless technologies, radio telescopes are struggling…

We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images. Similar to other challenges in computer vision domain…

Earth observation (EO) satellite missions have been providing detailed images about the state of the Earth and its land cover for over 50 years. Long term missions, such as NASA's Landsat, Terra, and Aqua satellites, and more recently, the…

In the past years modern mathematical methods for image analysis have led to a revolution in many fields, from computer vision to scientific imaging. However, some recently developed image processing techniques successfully exploited by…

Recovering high-fidelity images of the night sky from blurred observations is a fundamental problem in astronomy, where traditional methods typically fall short. In ground-based astronomy, combining multiple exposures to enhance…

Astronomical images are essential for exploring and understanding the universe. Optical telescopes capable of deep observations, such as the Hubble Space Telescope, are heavily oversubscribed in the Astronomical Community. Images also often…

I outline the prospects for performing weak lensing studies with the new generation of radio telescopes that are coming online now and in the future. I include a description of a proposed technique to use polarization observations in radio…

Context. Recently our ability to study stars using asteroseismic techniques has increased dramatically, largely through the use of space based photometric observations. Work has also been done using ground based spectroscopic observations…

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…

Emerging technologies can have major economic impacts and affect strategic stability. Yet, early identification of emerging technologies remains challenging. In order to identify emerging technologies in a timely and reliable manner, a…

This paper presents a novel metric to evaluate the robustness of deep learning based semantic segmentation approaches for crop row detection under different field conditions encountered by a field robot. A dataset with ten main categories…

Ship detection from satellite imagery using Deep Learning (DL) is an indispensable solution for maritime surveillance. However, applying DL models trained on one dataset to others having differences in spatial resolution and radiometric…

Astronomy is experiencing a rapid growth in data size and complexity. This change fosters the development of data-driven science as a useful companion to the common model-driven data analysis paradigm, where astronomers develop automatic…

Spectroscopic study of the night sky has been a common way to assess the impacts of artificial light at night at remote astronomical observatories. However, the spectroscopic properties of the urban night sky remain poorly documented. We…

In this paper, we report on our efforts for using Deep Learning for classifying artifacts and their features in digital visuals as a part of the Neoclassica framework. It was conceived to provide scholars with new methods for analyzing and…