Stojkovic Ana, Shopovska Ivana, Luong Hiep, Aelterman Jan, Jovanov Ljubomir, Philips Wilfried
TELIN-IPI, Ghent University-imec, 9000 Ghent, Belgium.
Sensors (Basel). 2019 Jul 21;19(14):3215. doi: 10.3390/s19143215.
Interpolation from a Color Filter Array (CFA) is the most common method for obtaining full color image data. Its success relies on the smart combination of a CFA and a demosaicing algorithm. Demosaicing on the one hand has been extensively studied. Algorithmic development in the past 20 years ranges from simple linear interpolation to modern neural-network-based (NN) approaches that encode the prior knowledge of millions of training images to fill in missing data in an inconspicious way. CFA design, on the other hand, is less well studied, although still recognized to strongly impact demosaicing performance. This is because demosaicing algorithms are typically limited to one particular CFA pattern, impeding straightforward CFA comparison. This is starting to change with newer classes of demosaicing that may be considered generic or CFA-agnostic. In this study, by comparing performance of two state-of-the-art generic algorithms, we evaluate the potential of modern CFA-demosaicing. We test the hypothesis that, with the increasing power of NN-based demosaicing, the influence of optimal CFA design on system performance decreases. This hypothesis is supported with the experimental results. Such a finding would herald the possibility of relaxing CFA requirements, providing more freedom in the CFA design choice and producing high-quality cameras.
从彩色滤光片阵列（CFA）进行插值是获取全彩色图像数据最常用的方法。其成功依赖于CFA和去马赛克算法的巧妙结合。一方面，去马赛克已经得到了广泛研究。过去20年里算法的发展从简单的线性插值到基于现代神经网络（NN）的方法，后者将数百万训练图像的先验知识进行编码，以不显眼的方式填补缺失数据。另一方面，CFA设计的研究较少，尽管人们仍然认识到它对去马赛克性能有很大影响。这是因为去马赛克算法通常限于一种特定的CFA模式，阻碍了直接的CFA比较。随着可能被认为是通用或与CFA无关的新型去马赛克算法的出现，这种情况开始发生变化。在本研究中，通过比较两种最先进的通用算法的性能，我们评估了现代CFA去马赛克的潜力。我们检验了这样一个假设，即随着基于NN的去马赛克能力的增强，最佳CFA设计对系统性能的影响会降低。实验结果支持了这一假设。这样的发现将预示着放宽CFA要求的可能性，在CFA设计选择上提供更多自由，并生产出高质量的相机。
