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Taylor Series-based Generic Demosaicking Algorithm for Multispectral Image

机译:基于泰勒级数的通用去马赛克算法

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Using coated mosaic video spectrometer to collect multispectral image which reduce the spectral information redundancy and data volume greatly and achieve real-time data transmission conditions. The mosaic video spectrometer imaging technique use a similar mosaic template to capture all the pixels and output a two-dimensional multi-spectral image with dozens of spectral information. The image is divided into a certain size of matrix in its field, and each pixel in the pixel matrix is only for one wavelength information response and every pixel response for different wavelength. The size of the pixel matrix block depends on the number of spectral segments, which results in a low spatial resolution of the single spectral segment image and the spectral information of each pixel absenting severely. Therefore, to reconstruct the complete multi-spectral image, we must estimate and interpolate the missing spatial information and spectral information by demosaicking multispectral image. In this paper, we present a novel demosaicking method to produce the high resolution multispectral image and reconstruct missing spectrum information in high accuracy. The proposed method computes the first-and second-order derivatives of the original single multispectral image to measure the geometry of edges in the image and the spectrum value of missing pixel. Two metrics are used to evaluate the generic algorithm, including the structural similarity index-measurement system (SSIM) for reconstruction performance and the procession time. Experimental results show that the demosaicked images present higher SSIM (more than 0.9) and comparable calculated time performance as traditional ways. This algorithm brings the greatest advantage that make up for the weakness of mosaick multispectral image and reduce the data transmission process cost and storage needs.
机译:使用涂层马赛克视频光谱仪采集多光谱图像,大大减少了光谱信息的冗余和数据量,达到了实时数据传输的条件。马赛克视频光谱仪成像技术使用类似的马赛克模板来捕获所有像素,并输出带有数十种光谱信息的二维多光谱图像。图像在其场中被划分为一定大小的矩阵,并且像素矩阵中的每个像素仅用于一个波长信息响应,而每个像素响应均用于不同波长。像素矩阵块的大小取决于光谱段的数量,这导致单个光谱段图像的空间分辨率低,并且每个像素的光谱信息严重缺失。因此,为了重建完整的多光谱图像,我们必须通过去马赛克多光谱图像来估计和内插缺失的空间信息和光谱信息。在本文中,我们提出了一种新颖的去马赛克方法,可产生高分辨率的多光谱图像并以高精度重建缺失的光谱信息。所提出的方法计算原始单个多光谱图像的一阶和二阶导数,以测量图像中边缘的几何形状和丢失像素的光谱值。使用两个度量来评估通用算法,包括用于重建性能和处理时间的结构相似性指数测量系统(SSIM)。实验结果表明,去马赛克图像具有更高的SSIM(大于0.9)和可比的传统时间计算时间性能。该算法带来的最大优势是弥补了镶嵌多光谱图像的不足,并降低了数据传输过程的成本和存储需求。

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