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}
}

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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

Cosmic-Ray Rejection by Laplacian Edge Detection

Conventional algorithms for rejecting cosmic rays in single CCD exposures rely on the contrast between cosmic rays and their surroundings and may produce erroneous results if the point-spread

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