Preparing Red‐Green‐Blue Images from CCD Data

By Robert Lupton, M. Blanton, G. Fekete, David W. Hogg, W. O'Mullane, A. Szalay, N. Wherry. 2003

289 citations recorded by the source corpus.

We present a new, and we believe arguably correct, algorithm for producing red‐green‐blue (RGB) composites from three‐band astronomical images. Our method ensures that an object with a specified astronomical color (e.g., g−r and r−i) has a unique color in the RGB image, as opposed to the burnt‐out white stars to which we are accustomed. A natural consequence of this is that we can use the same colors to code color‐magnitude diagrams, providing a natural “index” to our images. We also introduce the use of an arcsinh stretch that allows us to show faint objects while simultaneously preserving the structure of brighter objects in the field, such as the spiral arms of large galaxies. We believe that in addition to their aesthetic value, our images convey far more information than do the traditional ones, and we provide examples from Sloan Digital Sky Survey (SDSS) imaging, the Hubble Deep Field (HDF), and Chandra to support our claims.

Subfields: Computer Vision and Pattern Recognition, Ecology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/9452d677e3f101fa79616da959feeb98a9ef34f9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

  1. Kevin Schawinski
  2. M. Cappellari
  3. M. Sarzi
  4. Christopher J. Conselice
  5. D. Thomas
  6. R. McDermid
  7. C. Maraston
  8. Roger Davies

In Ecology

  1. F. V. D. Bosch
  2. H. Mo
  3. Xiaohu Yang
  4. M. Blanton
  5. J. Brinkmann
  6. Simon D. M. White
  7. D. Schneider
  8. G. Kauffmann

In Ocean Engineering

  1. M. Viel
  2. Robert A. Crain
  3. Joop Schaye
  4. Michelle Furlong
  5. N. Palanque-Delabrouille
  6. James Wadsley
  7. Tom Theuns
  8. Thomas R. Quinn

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