7 March 2014 Automating the design of image processing pipelines for novel color filter arrays: local, linear, learned (L3) method
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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.
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Qiyuan Tian, Qiyuan Tian, Steven Lansel, Steven Lansel, Joyce E. Farrell, Joyce E. Farrell, Brian A. Wandell, Brian A. Wandell, } "Automating the design of image processing pipelines for novel color filter arrays: local, linear, learned (L3) method", Proc. SPIE 9023, Digital Photography X, 90230K (7 March 2014); doi: 10.1117/12.2042565; https://doi.org/10.1117/12.2042565
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