This research assumes that the CFA patterns currently used are not necessarily optimal for deep learning instead of the traditional methods used in the demosaicing process in camera image processing, and attempts to find more suitable patterns. For deep learning, we used SwinIR, a super-resolution framework that is one of the most accurate in image processing, to search for the best CFA patterns for demosaicing. This revealed that the conventional approach of increasing the green component (G) was not optimal for demosaicing using SwinIR, and that patterns containing a fourth W component performed better than conventional Bayer patterns on the cPSNR and SSIM metrics. It was also found that W does not necessarily have to be luminance, but can be the average of the RGB values with comparable results.