Related papers: XAMI -- A Benchmark Dataset for Artefact Detection…
X-ray Multi-Mirror Mission (XMM-Newton) has been one of the most successful astronomy missions launched by the European Space Agency. It exploits innovative use of replication technology for the X-ray reflecting telescopes that has resulted…
Modern astronomy relies on massive databases collected by robotic telescopes and digital sky surveys, acquiring data in a much faster pace than what manual analysis can support. Among other data, these sky surveys collect information about…
The Optical Monitor Catalogue of serendipitous sources (OMCat) contains entries for every source detected in the publicly available XMM-Newton Optical Monitor (OM) images taken in either the imaging or ``fast'' modes. Since the OM is…
Thanks to the large collecting area (3 x ~1500 cm$^2$ at 1.5 keV) and wide field of view (30' across in full field mode) of the X-ray cameras on board the European Space Agency X-ray observatory XMM-Newton, each individual pointing can…
Ring artifacts in X-ray micro-CT images are one of the primary causes of concern in their accurate visual interpretation and quantitative analysis. The geometry of X-ray micro-CT scanners is similar to the medical CT machines, except the…
Accurate photometry in astronomical surveys is challenged by image artefacts, which affect measurements and degrade data quality. Due to the large amount of available data, this task is increasingly handled using machine learning…
The XMM-Newton Survey Science Centre Consortium (SSC) develops software in close collaboration with the Science Operations Centre to perform a pipeline analysis of all XMM-Newton observations. In celebration of the 20th launch anniversary,…
The XMM identification programme (XID) is accumulating Optical/IR images and spectra to identify thousands of XMM sources. The XID Database is the main repository of this data and permits easy selection and correlation of XMM X-ray and UV…
We present a two-component Machine Learning (ML) based approach for classifying astronomical images by data-quality via an examination of sources detected in the images and image pixel values from representative sources within those images.…
Astronomical images provide information about the great variety of cosmic objects in the Universe. Due to the large volumes of data, the presence of innumerable bright point sources as well as noise within the frame and the spatial gap…
The field of artificial intelligence based image enhancement has been rapidly evolving over the last few years and is able to produce impressive results on non-astronomical images. In this work we present the first application of Machine…
We propose a new method for solving an important problem of astronomy that arises in observations with ultrahigh-angular-resolution interferometers. This method is based on the application of the theory of artificial neural networks. We…
Object detection has witnessed significant progress by relying on large, manually annotated datasets. Annotating such datasets is highly time consuming and expensive, which motivates the development of weakly supervised and few-shot object…
Atomic Force Microscopy (AFM) enables high-resolution surface imaging at the nanoscale, yet the output is often degraded by artifacts introduced by environmental noise, scanning imperfections, and tip-sample interactions. To address this…
Survey telescopes such as the Vera C. Rubin Observatory and the Square Kilometre Array will discover billions of static and dynamic astronomical sources. Properly mined, these enormous datasets will likely be wellsprings of rare or unknown…
Current AI-Generated Image (AIGI) detection approaches predominantly rely on binary classification to distinguish real from synthetic images, often lacking interpretable or convincing evidence to substantiate their decisions. This…
Modern astronomical surveys are producing datasets of unprecedented size and richness, increasing the potential for high-impact scientific discovery. This possibility, coupled with the challenge of exploring a large number of sources, has…
XMM-Newton has observed the X-ray sky since early 2000. The XMM-Newton Survey Science Centre Consortium has published catalogues of X-ray and ultraviolet sources found serendipitously in the individual observations. This series is now…
Environmental and instrumental conditions can cause anomalies in astronomical images, which can potentially bias all kinds of measurements if not excluded. Detection of the anomalous images is usually done by human eyes, which is slow and…
Artifact removal is an integral component of cinematic scientific visualization, and is especially challenging with big datasets in which artifacts are difficult to define. In this paper, we describe a method for creating cloud artifact…