A new in-camera imaging model for color computer vision and its application

Seon Joo Kim, Hai Ting Lin, Zheng Lu, Sabine Süsstrunk, Stephen Lin, Michael S. Brown

Research output: Journal PublicationArticlepeer-review

128 Citations (Scopus)


We present a study of in-camera image processing through an extensive analysis of more than 10,000 images from over 30 cameras. The goal of this work is to investigate if image values can be transformed to physically meaningful values, and if so, when and how this can be done. From our analysis, we found a major limitation of the imaging model employed in conventional radiometric calibration methods and propose a new in-camera imaging model that fits well with today's cameras. With the new model, we present associated calibration procedures that allow us to convert sRGB images back to their original CCD RAW responses in a manner that is significantly more accurate than any existing methods. Additionally, we show how this new imaging model can be used to build an image correction application that converts an sRGB input image captured with the wrong camera settings to an sRGB output image that would have been recorded under the correct settings of a specific camera.

Original languageEnglish
Article number6158647
Pages (from-to)2289-2302
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number12
Publication statusPublished - 2012
Externally publishedYes


  • Radiometric calibration
  • gamut mapping
  • in-camera image processing
  • white balance

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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