CAJ | 학술논문

压缩感知重建是解决高光谱现有成像模式数据量大冗余度高问题的一个有效机制。针对高光谱图像的多通道特性，该文建立了高光谱压缩感知的多测量向量模型，编码端使用随机卷积算子对各通道进行快速采样，生成测量向量矩阵。解码端构建图稀疏正则化的联合重建模型，在稀疏变换域将高光谱图像分解为谱间的关联成分和差异成分，通过图结构化稀疏度量表征关联成分的空谱相关性，并约束谱间差异成分的稀疏性。进一步提出模型求解的交替方向乘子迭代算法，通过引入辅助变量与线性化技巧，使得每一子问题均存在解析解，降低了模型求解的复杂度。对多个实测数据集进行了对比实验，实验结果验证了该文模型与算法的有效性。
압축감지중건시해결고광보현유성상모식수거량대용여도고문제적일개유효궤제。침대고광보도상적다통도특성，해문건립료고광보압축감지적다측량향량모형，편마단사용수궤권적산자대각통도진행쾌속채양，생성측량향량구진。해마단구건도희소정칙화적연합중건모형，재희소변환역장고광보도상분해위보간적관련성분화차이성분，통과도결구화희소도량표정관련성분적공보상관성，병약속보간차이성분적희소성。진일보제출모형구해적교체방향승자질대산법，통과인입보조변량여선성화기교，사득매일자문제균존재해석해，강저료모형구해적복잡도。대다개실측수거집진행료대비실험，실험결과험증료해문모형여산법적유효성。
Compressed Sensing (CS) reconstruction of hyperspectral image is an effective mechanism to comedy the traditional hyperspectral imaging pattern with the drawback of high redundancy and vast data volume. This paper presents a new multiple measurement vector model for compressed sensing reconstruction of hyperspectral data in consideration of its multiple channel character. In the encoding side, the random convolution operator is used to rapidly obtain the measurement vector of each channel which is subsequently reorganized as a measurement vector matrix. In the decoding side, a joint reconstruction model is proposed to reconstruct the hyperspectral data from the multiple measurement vectors. The model decomposes the hyperspectral data into the inter-channel correlated and differenced component in the sparsifying transform domain, where the correlated component with high spatial and spectral correlation is constrained to be graph structured sparse and the differenced component is constrained to be l1 sparse. A numerical optimization algorithm is also proposed to solve the reconstruction model by the alternating direction method of multiplier. Every sub-problem in the iteration formula admits analysis solution by introducing the auxiliary variable and linearization operation. The complexity of the numerical optimization algorithm is reduced. The experimental results demonstrate the effectiveness of the proposed algorithm.