Thursday, June 18, 2020

Low Light Imaging with CFA 3.0

Applied Research LLC; Rockville, MD, USA publishes MDPI paper "Demosaicing of CFA 3.0 with Applications to Low Lighting Images" by by Chiman Kwan, Jude Larkin, and Bulent Ayhan.

"Low lighting images usually contain Poisson noise, which is pixel amplitude-dependent. More panchromatic or white pixels in a color filter array (CFA) are believed to help the demosaicing performance in dark environments. In this paper, we first introduce a CFA pattern known as CFA 3.0 that has 75% white pixels, 12.5% green pixels, and 6.25% of red and blue pixels. We then present algorithms to demosaic this CFA, and demonstrate its performance for normal and low lighting images.

In addition, a comparative study was performed to evaluate the demosaicing performance of three CFAs, namely the Bayer pattern (CFA 1.0), the Kodak CFA 2.0, and the proposed CFA 3.0. Using a clean Kodak dataset with 12 images, we emulated low lighting conditions by introducing Poisson noise into the clean images. In our experiments, normal and low lighting images were used. For the low lighting conditions, images with signal-to-noise (SNR) of 10 dBs and 20 dBs were studied. We observed that the demosaicing performance in low lighting conditions was improved when there are more white pixels.

Moreover, denoising can further enhance the demosaicing performance for all CFAs. The most important finding is that CFA 3.0 performs better than CFA 1.0, but is slightly inferior to CFA 2.0, in low lighting images."

1 comment:

  1. Poor bright light/noiseless performance was the main problem faced by previous RGBW implementations. CFA 3.0 being worse than CFA 2.0 in the noiseless case is a step in the wrong direction for market acceptance. Low light performance is already very good for CFA 2.0 and is not the gating factor at this point in time. Perhaps, in the future, CFA 3.0 will be useful.

    These have been our (Image Algorithmics) findings from partnering with image sensor companies, developing small pixel prototype RGBW sensors and processing algorithms that met their quality standards.

    High luminance and chrominance resolution, low false color and good dynamic range are essential. Non-binnable RGBW CFAs need to be competitive with Bayer in bright light, while binnable RGBW CFAs need to be competitive with Quad Bayer.


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