基于双频带预测的高光谱压缩感知重构算法

Hyperspectral compressed sensing reconstruction algorithm based on dual-band prediction

为改善高光谱压缩感知的重构质量,提出基于双频带预测的高光谱重构算法。引入K均值聚类算法自适应地完成频带分组,并确定各组组内的双参考频带;再建立双频带预测模型获得预测图像;最后,在预测图像的基础上采用修正重构和加权融合方式,实现图像的高精确度重构。结果表明:在相同采样率下,该方法的重构图像的峰值信噪比(PSNR)和结构相似性(SSIM)明显优于已有的重构方法。

To improve the reconstruction quality of hyperspectral compression sensing, a hyperspectral reconstruction algorithm based on dual-band prediction is proposed. K-means clustering algorithm is introduced to adaptively complete the band grouping, and the dual reference bands in the group are determined. Dual band prediction model is employed to obtain the initial prediction image. Based on the predicted image, the modified reconstruction and weighted fusion method are utilized to achieve highprecision image reconstruction. The results show that at the same sampling rate, the reconstructed image of this method has better Peak Signal to Noise Ratio(PSNR) and Structural SIMilarity(SSIM) compared with that of the existing reconstruction method.