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Open AccessFeature PaperArticle

Demosaicing of Bayer and CFA 2.0 Patterns for Low Lighting Images

Applied Research LLC, Rockville, MD 20850, USA
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Electronics 2019, 8(12), 1444; https://doi.org/10.3390/electronics8121444
Received: 26 October 2019 / Revised: 24 November 2019 / Accepted: 26 November 2019 / Published: 1 December 2019
(This article belongs to the Section Circuit and Signal Processing)
It is commonly believed that having more white pixels in a color filter array (CFA) will
help the demosaicing performance for images collected in low lighting conditions. However, to the
best of our knowledge, a systematic study to demonstrate the above statement does not exist. We
present a comparative study to systematically and thoroughly evaluate the performance of
demosaicing for low lighting images using two CFAs: the standard Bayer pattern (aka CFA 1.0) and
the Kodak CFA 2.0 (RGBW pattern with 50% white pixels). Using the clean Kodak dataset
containing 12 images, we first emulated low lighting images by injecting Poisson noise at two signalto-
noise (SNR) levels: 10 dBs and 20 dBs. We then created CFA 1.0 and CFA 2.0 images for the noisy
images. After that, we applied more than 15 conventional and deep learning based demosaicing
algorithms to demosaic the CFA patterns. Using both objectives with five performance metrics and
subjective visualization, we observe that having more white pixels indeed helps the demosaicing
performance in low lighting conditions. This thorough comparative study is our first contribution.
With denoising, we observed that the demosaicing performance of both CFAs has been improved
by several dBs. This can be considered as our second contribution. Moreover, we noticed that
denoising before demosaicing is more effective than denoising after demosaicing. Answering the
question of where denoising should be applied is our third contribution. We also noticed that
denoising plays a slightly more important role in 10 dBs signal-to-noise ratio (SNR) as compared to
20 dBs SNR. Some discussions on the following phenomena are also included: (1) why CFA 2.0
performed better than CFA 1.0; (2) why denoising was more effective before demosaicing than after
demosaicing; and (3) why denoising helped more at low SNRs than at high SNRs.
Keywords: debayering; demosaicing; color filter array (CFA); RGBW pattern; Bayer pattern; CFA 1.0; CFA2.0; pansharpening; deep learning debayering; demosaicing; color filter array (CFA); RGBW pattern; Bayer pattern; CFA 1.0; CFA2.0; pansharpening; deep learning
MDPI and ACS Style

Kwan, C.; Larkin, J. Demosaicing of Bayer and CFA 2.0 Patterns for Low Lighting Images. Electronics 2019, 8, 1444.

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