The Bayer and other commercial CFA patterns have been empirically optimized. Recent research has allowed IA to mathematically analyze CFAs and methodically optimize them. This optimization is carried out with the 3 primary objectives of spatial spectrum shaping, spatial frequency assignment of luminance, chrominance signals to minimize crosstalk between them and minimization of noise amplification during demosaicking, as well as with the secondary objective of maximizing light transmissivity through the color filters. The CFA pattern shown below is the culmination of these efforts.
This pattern differs from the Bayer pattern not only in the filter colors but also in the geometry of pixels. Instead of square pixels, the proposed pattern has rectangular pixels of the same area but aspect ratio of 1.41:1. This is the same shape as an A4 sheet of paper. The output image has the same resolution in all directions and this resolution is equal to the horizontal Nyquist limit of the sensor and greater than the OLPF limited resolution captured by leading Bayer cameras. The finer vertical pixel pitch of the sensor is not used to capture extra vertical detail, but is instead used to capture color information. The output image is in square pixels so that the rectangular photosite shape is internal to the camera and not exposed to the user.
In comparison to the Bayer CFA with the same pixel count, similar luminance and chrominance resolution, and demosaicked with the popular AHD algorithm, the proposed pattern has the following advantages: