March 2016
Spotlight Summary by Roarke Horstmeyer
Rank-based camera spectral sensitivity estimation
Should I filter that picture using the “noir” or the “instant” look? It is increasingly important to get the color just right in our digital images — or at least it appears to be so for most Instagram users. However, it is also a pretty challenging engineering task. Each type of digital camera converts light to an electric signal in a slightly different way. Color filtering is an important step in this process, and the unique color filters used in each camera type produce a particular response curve. Currently, this color response curve must be carefully calibrated for each new batch of cellphones. Unfortunately, current calibration methods can be somewhat laborious. Furthermore, other changes made to the image with software (e.g., how the image is compressed, sharpened or toned) can also potentially alter the color response. In this JOSA A article, Finlayson and his co-authors propose a new, simple strategy to estimate camera color sensitivity. Their novel algorithm requires just one image of a color chart. It then compares different color measurements to one another and backs out the camera response curve, while remaining invariant to a large class of software manipulations. In the end, this technique should lead to a quick and easy way to make sure your images are consistently well-colored, which will hopefully get you some more “likes” from your friends.
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Article Information
Rank-based camera spectral sensitivity estimation
Graham Finlayson, Maryam Mohammadzadeh Darrodi, and Michal Mackiewicz
J. Opt. Soc. Am. A 33(4) 589-599 (2016) View: Abstract | HTML | PDF