基于改进SL0算法的MSFA模式多光谱图像去马赛克方法

针对MSFA模式多光谱图像去马赛克算法精度较低和计算复杂等缺点,利用压缩感知理论在信号恢复方面的优势,提出一种新的光谱图像去马赛克算法。采用随机模式的多光谱滤波阵列MSFA获得马赛克图像,通过将MSFA采样值等效为压缩感知理论中的感知矩阵采样所得数据,将去马赛克问题转化为压缩感知稀疏信号恢复问题,并利用多光谱图像的谱间相关性,给出基于压缩感知框架的多光谱图像去马赛克模型,最后采用改进的光滑0范数算法求解去马赛克问题,得到重构的多光谱图像。客观评价指标显示,该算法的峰值信噪比值相较于克罗内克压缩感知和组稀疏两种算法有明显提高;主观评价结果表明,该算法能有效减少重构图像中的锯齿现象,具有更好的视觉效果。

Multi-Spectral Demosaicking Method Based on MSFA Pattern and Improved SL0 Algorithm

In order to overcome the shortcomings such as low accuracy and high complexity of multi-spectral image demosaicking algorithm in MSFA pattern, a new method of spectral image demosaicking algorithm was proposed based on the advantage of compressed sensing theory in signal reconstruction. Mosaic images were obtained by using a random MSFA pattern. The sampling value of MSFA was equivalent to the data obtained from the perceptual matrix sampling in the compressed sensing theory. The problem of multi-spectral demosaicking of MSFA pattern was transformed into the problem of sparse signal reconstruction in compressed sensing and the spectral correlation of multi-spectral images was utilized. A framework of multi-spectral demosaicking based on compressive sensing was presented. Finally, the optimization method was used to solve the problem of the 0 norm for recovering the multi-spectral image. The objective evaluation results showed that the peak signal to noise ratio of the algorithm was significantly improved compared with those of two algorithms based on Kronecker and the group sparse. Subjective evaluation indicated it could effectively reduce the aliasing in the reconstructed image with better visual effect.