Detection and removal of artifacts in astronomical images

@article{Desai2016DetectionAR,
  title={Detection and removal of artifacts in astronomical images},
  author={Shantanu Desai and Joseph J. Mohr and Emmanuel Bertin and Martin Kuemmel and Markus Wetzstein},
  journal={Astron. Comput.},
  year={2016},
  volume={16},
  pages={67-78}
}
View PDF on arXiv
23 Citations
Background Citations
10
Methods Citations
4

Figures and Tables from this paper

23 Citations

DeepGhostBusters: Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts from optical survey images

Spatial field reconstruction with INLA: application to IFU galaxy data

Astronomical observations of extended sources, such as cubes of integral field spectroscopy (IFS), encode auto-correlated spatial structures that cannot be optimally exploited by standard…

Detecting unresolved binary stars in Euclid VIS images

Measuring a weak gravitational lensing signal to the level required by the next generation of space-based surveys demands exquisite reconstruction of the point-spread function (PSF). However,…

Cosmic Ray Detection in Astronomical Images via Dictionary Learning and Sparse Representation

This work proposes a novel Dictionary Learning (DL) based framework to detect Cosmic Ray (CR) hits that contaminate the astronomical images obtained through optical photometric surveys and evaluates the performance of proposed DL-guided deep segmentation models over the baseline on test data from DECam.

Expediting DECam Multimessenger Counterpart Searches with Convolutional Neural Networks

The number of images requiring human inspection will decrease by a factor of 1.5 using the convolutional neural networks approach alone and by a factors of 3.6 using the approach in combination with existing algorithms, facilitating rapid multimessenger counterpart identification by the astronomical community.

Galactic cirri in deep optical imaging

The ubiquitous presence of Galactic cirri in deep optical images represents a major obstacle to study the low surface brightness features of extragalactic sources. To address this issue, we have…

The Dark Energy Survey Image Processing Pipeline

The Dark Energy Survey (DES) is a five-year optical imaging campaign with the goal of understanding the origin of cosmic acceleration. DES performs a ∼5000 deg2 survey of the southern sky in five…

Cosmic Ray rejection with attention augmented deep learning

A SLIC-DBSCAN Based Algorithm for Extracting Effective Sky Region from a Single Star Image

A SLIC-DBSCAN-based algorithm for extracting effective information from a single image with strong interference has been developed in this paper to remove interferences and extract star spots from the sky region of the real star image.

deepCR: Cosmic Ray Rejection with Deep Learning

DeepCR, a deep learning based framework for cosmic ray (CR) identification and subsequent image inpainting based on the predicted CR mask achieves close to 100% detection rates in both extragalactic and globular cluster fields, and 91% in resolved galaxy fields, which is a significant improvement over current state-of-the-art method, LACosmic.

References

SHOWING 1-10 OF 39 REFERENCES

Implementation of Robust Image Artifact Removal in SWarp through Clipped Mean Stacking

An algorithm for detecting and removing artifacts from astronomical images by means of outlier rejection during stacking that has superior noise properties, allowing a significant reduction in exposure time compared to median stacking.

Simultaneous Multicolor Detection of Faint Galaxies in the Hubble Deep Field

We present a novel way to detect objects when multiband images are available. Typically, object detection is performed in one of the available bands or on a somewhat arbitrarily co-added image. Our…

Cosmic‐Ray Rejection by Linear Filtering of Single Images

It is demonstrated that the false alarm probability for a pixel containing object flux will never exceed the corresponding probability for an blank-sky pixel, provided the convolution kernel appropriately, which allows confident rejection of cosmic rays superposed on real objects.

Evaluation of Cosmic Ray Rejection Algorithms on Single-Shot Exposures

Abstract To maximise data output from single-shot astronomical images, the rejection of cosmic rays is important. We present the results of a benchmark trial comparing various cosmic ray rejection…

Drizzle: A Method for the Linear Reconstruction of Undersampled Images

The photometric and astrometric accuracy and image fidelity of the algorithm as well as the noise characteristics of output images are discussed and the use of drizzling to combine dithered images in the presence of cosmic rays is described.

Automatic removal of cosmic ray signatures in Deep Impact images

The Dark Energy Survey data processing and calibration system

The Dark Energy Survey (DES) is a 5000 deg2 grizY survey reaching characteristic photometric depths of 24th magnitude (10 sigma) and enabling accurate photometry and morphology of objects ten times…

THE BLANCO COSMOLOGY SURVEY: DATA ACQUISITION, PROCESSING, CALIBRATION, QUALITY DIAGNOSTICS, AND DATA RELEASE

The Blanco Cosmology Survey (BCS) is a 60 night imaging survey of ∼80 deg2 of the southern sky located in two fields: (α, δ) = (5 hr, −55°) and (23 hr, −55°). The survey was carried out between 2005…

The Two Micron All Sky Survey (2MASS)

Between 1997 June and 2001 February the Two Micron All Sky Survey (2MASS) collected 25.4 Tbytes of raw imaging data covering 99.998% of the celestial sphere in the near-infrared J (1.25 μm), H (1.65…

CosmoDM and its application to Pan-STARRS data

Processed Pan-STARRS data from CosmoDM has been used to optically confirm and measure photometric redshifts of Planck-based Sunyaev-Zeldovich effect selected cluster candidates.