电子学报Issue(11)：2219-2224,6.DOI:10.3969/j.issn.0372-2112.2014.11.014
联合低秩与稀疏先验的高光谱图像压缩感知重建
Compressed Sensing Reconstruction of Hyperspectral Imagery Jointly Using Low Rank and Sparse Prior
摘要
Abstract
A new compressed sensing model is proposed to reconstruct hyperspectral image .In the encoder side ,block-dialog measurement matrix formed by permuted noiselets transform is used to randomly measure the signal of each channel independently . In the decoder side ,the low rank and sparse representation models are firstly constructed to decompose hyperspectral data matrix into low rank and sparse parts ,and the low rank part is further sparsely decomposed .Then ,the intra-channel low rank prior and the inter-channel sparse prior are jointly utilized to reconstruct the compressed data .A numerical optimization algorithm is also proposed to solve the reconstruction model by augmented lagrange multiplier method .Every sub-problem in the iteration formula admits analyti-cal solution after introducing auxiliary variable and linearization operation .The complexity of the numerical optimization algorithm is reduced .The experimental results verify the effectiveness of our algorithm .关键词
压缩感知/低秩先验/稀疏先验/增广拉格朗日乘子算法Key words
compressed sensing/low rank prior/sparse prior/augmented Lagrange multiplier method分类
信息技术与安全科学引用本文复制引用
孙玉宝,吴泽彬,吴敏,刘青山..联合低秩与稀疏先验的高光谱图像压缩感知重建[J].电子学报,2014,(11):2219-2224,6.基金项目
国家自然科学基金（No ．61300162，No ．81201161）；江苏省自然科学基金（No ．BK2012045，No ．BK20131003）；中国博士后基金（No ．20110491429）；江苏省博士后基金（No ．1101083C）；江苏省光谱成像与智能感知重点实验室基金 （）
