Kawase Maru, Shinoda Kazuma, Hasegawa Madoka
IEEE Trans Image Process. 2019 Oct;28(10):4984-4996. doi: 10.1109/TIP.2019.2910392. Epub 2019 Apr 16.
Multispectral imaging with a multispectral filter array (MSFA) facilitates snapshot imaging; however, a demosaicking process is required to estimate a fully defined multispectral image based on undersampled sensor data. Undersampling induces aliasing and adverse artifacts in the reconstructed image. To solve this problem, Jia et al. proposed the Fourier spectral filter array (FSFA), which can reduce aliasing. In this paper, we analyze the FSFA and a more generalized anti-aliasing MSFA, and we identify the property that makes MSFAs anti-aliasing. Furthermore, we propose a novel demosaicking method that is a hybrid of frequency-decomposition-based and compressive-sensing-based demosaicking. Anti-aliasing MSFAs enable demosaicking to comprehend the precise spatial structures of an image. The image assists our proposed method in precisely reconstructing images using compressive sensing. Our experimental results demonstrated that the proposed method performs better than the existing demosaicking methods, especially in terms of spatial reconstruction.
使用多光谱滤波器阵列（MSFA）的多光谱成像有助于快照成像；然而，需要一个去马赛克过程来基于欠采样的传感器数据估计一个完全定义的多光谱图像。欠采样会在重建图像中引起混叠和不良伪像。为了解决这个问题，Jia等人提出了傅里叶光谱滤波器阵列（FSFA），它可以减少混叠。在本文中，我们分析了FSFA和一种更通用的抗混叠MSFA，并确定了使MSFA具有抗混叠特性的属性。此外，我们提出了一种新颖的去马赛克方法，它是基于频率分解和基于压缩感知的去马赛克的混合方法。抗混叠MSFA使去马赛克能够理解图像的精确空间结构。该图像有助于我们提出的方法使用压缩感知精确地重建图像。我们的实验结果表明，所提出的方法比现有的去马赛克方法表现更好，特别是在空间重建方面。
