Abstract
In CFA 2.0, there are white pixels in a color filter array (CFA) that has proven to help the demosaicing performance for images collected in low light conditions. Here, we evaluate the performance of demosaicing for images collected in low light conditions using an RGBW pattern with 75% white pixels. We term this CFA the CFA 3.0. Using a data set containing 10 images collected in low light conditions, we performed extensive experiments. Both objective and subjective evaluations were used in our experiments.
Highlights
The standard Bayer pattern [1], known as color filter array (CFA) 1.0, has been widely used in many commercial cameras
The three best methods used for F3 are Demonet + GS Adaptive (GSA), Principal Component Analysis (PCA), and Guided Filter PCA (GFPCA)
For images collected in normal illumination conditions, our earlier papers [11] and [29] concluded that CFA 1.0 is better than CFA 2.0
Summary
The standard Bayer pattern [1], known as color filter array (CFA) 1.0, has been widely used in many commercial cameras. Emulated low lighting images were used in our experiments. In our recent paper on the demosaicing of CFA 2.0 (RGBW) [11], we have extensively compared CFA 1.0 and CFA 2.0 using IMAX and Kodak images and observed that CFA 1.0 has better performance. We performed another study using emulated low lighting images in [10]. Our experiments showed that CFA 2.0 and CFA 3.0 have lower noise than CFA 1.0 in actual low lighting images. The four R, G, B pixels are extracted first and a demosaicing algorithm for CFA 1.0 is applied. Afterwards, any pansharpening algorithms can be used to fuse the pan and the demosaiced reduced resolution image to generate a full resolution color image. We have two pixel-level fusion algorithms known as F3 [29] and ATMF [30]
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