BLIND COMPRESSIVE HYPER-SPECTRAL IMAGING

Compressive hyperspectral imaging is an inverse problem of reconstructing a hyperspectral image from its low-dimensional measurements. Single-pixel architecture has been extended in various studies to acquire compressive hyperspectral images. Compressed sensing requires prior knowledge about sparsifying transform domain in which signal has sparse representation. This work utilizes Blind Compressed Sensing (BCS) framework which does not require any fixed sparsifying transform to sparsify hyperspectral image. The proposed method outperforms the existing Kronecker compressed sensing based method.