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Green Image
Issue No. 12 - Dec. (2012 vol. 34)
ISSN: 0162-8828
pp: 2289-2302
S. Lin , Microsoft Res. Asia, Beijing, China
Sabine Süsstrunk , IVRG, EPFL, Lausanne, Switzerland
Zheng Lu , Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore
Hai Ting Lin , Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore
Seon Joo Kim , SUNY Korea, Incheon, South Korea
M. S. Brown , Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore
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.
image colour analysis, calibration, CCD image sensors, computer vision, image correction application, in-camera imaging model, color computer vision, in-camera image processing, imaging model, radiometric calibration methods, sRGB images, CCD RAW responses, Image color analysis, Cameras, Image processing, Calibration, Radiometry, Computational modeling, white balance, Radiometric calibration, in-camera image processing, gamut mapping
S. Lin, Sabine Süsstrunk, Zheng Lu, Hai Ting Lin, Seon Joo Kim, M. S. Brown, "A New In-Camera Imaging Model for Color Computer Vision and Its Application", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 2289-2302, Dec. 2012, doi:10.1109/TPAMI.2012.58
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