Oh Paul, Lee Sukho, Kang Moon Gi
Department of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-Ku, Seoul 03722, Korea.
Department of Software Engineering, Dongseo University, 47 Jurye-ro, Sasang-Ku, Busan 47011, Korea.
Sensors (Basel). 2017 Jun 28;17(7):1523. doi: 10.3390/s17071523.
Recently, several RGB-White (RGBW) color filter arrays (CFAs) have been proposed, which have extra white (W) pixels in the filter array that are highly sensitive. Due to the high sensitivity, the W pixels have better SNR (Signal to Noise Ratio) characteristics than other color pixels in the filter array, especially, in low light conditions. However, most of the RGBW CFAs are designed so that the acquired RGBW pattern image can be converted into the conventional Bayer pattern image, which is then again converted into the final color image by using conventional demosaicing methods, i.e., color interpolation techniques. In this paper, we propose a new RGBW color filter array based on a totally different color interpolation technique, the colorization algorithm. The colorization algorithm was initially proposed for colorizing a gray image into a color image using a small number of color seeds. Here, we adopt this algorithm as a color interpolation technique, so that the RGBW color filter array can be designed with a very large number of W pixels to make the most of the highly sensitive characteristics of the W channel. The resulting RGBW color filter array has a pattern with a large proportion of W pixels, while the small-numbered RGB pixels are randomly distributed over the array. The colorization algorithm makes it possible to reconstruct the colors from such a small number of RGB values. Due to the large proportion of W pixels, the reconstructed color image has a high SNR value, especially higher than those of conventional CFAs in low light condition. Experimental results show that many important information which are not perceived in color images reconstructed with conventional CFAs are perceived in the images reconstructed with the proposed method.
最近，已经提出了几种RGB-白色（RGBW）彩色滤光片阵列（CFA），其在滤光片阵列中具有额外的高灵敏度白色（W）像素。由于灵敏度高，W像素比滤光片阵列中的其他颜色像素具有更好的信噪比（SNR）特性，特别是在低光照条件下。然而，大多数RGBW CFA的设计使得获取的RGBW图案图像可以转换为传统的拜耳图案图像，然后再通过使用传统的去马赛克方法（即颜色插值技术）转换为最终的彩色图像。在本文中，我们基于一种完全不同的颜色插值技术——着色算法，提出了一种新的RGBW彩色滤光片阵列。着色算法最初是为了使用少量颜色种子将灰度图像转换为彩色图像而提出的。在这里，我们采用该算法作为颜色插值技术，以便可以设计具有大量W像素的RGBW彩色滤光片阵列，以充分利用W通道的高灵敏度特性。所得的RGBW彩色滤光片阵列具有W像素比例很大的图案，而少量的RGB像素随机分布在阵列上。着色算法使得可以从如此少量的RGB值重建颜色。由于W像素比例很大，重建的彩色图像具有很高的SNR值，特别是在低光照条件下高于传统CFA的SNR值。实验结果表明，在用传统CFA重建的彩色图像中未被感知的许多重要信息在用所提出的方法重建的图像中被感知到了。
