一种八谱段滤光片成像系统设计

针对多光谱滤光片阵列成像采样率低,原始(Raw)数据稀疏所导致的重建图像模糊,高频信息丢失等问题,提出了一种新八谱段滤光片阵列分布方案,利用基于邻域梯度延伸方法对光谱Raw图像进行重建.首先基于二叉树生成法,在重复排列的4×4阵列中设计了一种等空间概率比的八谱段滤光片分布方案;然后针对传感器直接获取的稀疏Raw图像,计算各谱段采样点的梯度信息,在保持图像结构特征和纹理信息的基础上,利用邻域采样点的像素值和梯度值对未采样点进行重建,从而获得完整的光谱图像信息;最后,基于已重建的八谱段光谱图像,采用伪逆矩阵法重构各像素位置的31波段光谱值.结果表明,相对于主流图像重建方法,本文算法提高了重建八谱段光谱图像的峰值信噪比、复合峰值信噪比,降低了光谱均方差,更好地保留了图像的纹理和边缘,有效降低了多光谱滤光片阵列成像中的颜色伪影和图像模糊等现象.

Design of an Eight-band Filter Imaging System

Aiming at the problems of fuzzy reconstructed image and loss of high-frequency information caused by low sampling rate of multi-spectral filter array,sparse Raw data and so on,a new eightspectral filter array distribution scheme is proposed,which is based on neighborhood gradient extension method to reconstruct the spectral Raw image.Firstly,based on the binary tree generation method,an eight-band filter distribution scheme with equal spatial probability ratio is designed in the 4×4 array of repetitive arrays.Then,for the sparse Raw image directly acquired by the sensor,the gradient information of sampling points in each spectrum segment is calculated.On the basis of maintaining the image structure features and texture information,the pixel value and gradient value of sampling points in the neighborhood are used to reconstruct the unsampled points,so as to obtain the complete spectral image information.Finally,based on the reconstructed eight-band spectral images,the pseudo-inverse matrix method is used to reconstruct the 31-band spectral values of each pixel position.The results show that compared with the mainstream image reconstruction methods,the proposed algorithm improves the peak signal-to-noise ratio and composite peak signal-to-noise ratio of reconstructed eight-band spectral images,reduces the mean square error of the spectrum,and better preserves the texture and edges of the image.Reduced artifacts such as color artifacts and image blur in multi-spectral filter array imaging.