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Proceedings Paper

Automating the design of image processing pipelines for novel color filter arrays: local, linear, learned (L3) method
Author(s): Qiyuan Tian; Steven Lansel; Joyce E. Farrell; Brian A. Wandell
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Paper Abstract

The high density of pixels in modern color sensors provides an opportunity to experiment with new color filter array (CFA) designs. A significant bottleneck in evaluating new designs is the need to create demosaicking, denoising and color transform algorithms tuned for the CFA. To address this issue, we developed a method(local, linear, learned or L3) for automatically creating an image processing pipeline. In this paper we describe the L3 algorithm and illustrate how we created a pipeline for a CFA organized as a 2×2 RGB/Wblock containing a clear (W) pixel. Under low light conditions, the L3 pipeline developed for the RGB/W CFA produces images that are superior to those from a matched Bayer RGB sensor. We also use L3 to learn pipelines for other RGB/W CFAs with different spatial layouts. The L3 algorithm shortens the development time for producing a high quality image pipeline for novel CFA designs.

Paper Details

Date Published: 7 March 2014
PDF: 8 pages
Proc. SPIE 9023, Digital Photography X, 90230K (7 March 2014); doi: 10.1117/12.2042565
Show Author Affiliations
Qiyuan Tian, Stanford Univ. (United States)
Steven Lansel, Olympus America Inc. (United States)
Joyce E. Farrell, Stanford Univ. (United States)
Brian A. Wandell, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 9023:
Digital Photography X
Nitin Sampat; Radka Tezaur; Sebastiano Battiato; Boyd A. Fowler, Editor(s)

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