Compressive hyper-spectral imaging in the presence of real noise

Compressed sensing has found several applications in hyperspectral imaging because it helps in reducing the size of data to be captured or to be transmitted to ground stations. This work is based on the reconstruction of a hyperspectral image from compressive measurements. There are various hardware models proposed in the literature for compressed sensing of hyperspectral images. This work considers the reconstruction of full hyperspectral images from its compressive measurements when they are contaminated by mixed noise. A generic mixed noise model has been considered that explicitly accounts for the presence of both Gaussian and impulse noise. The quantitative and qualitative experimental results with synthetic and real image demonstrate the capabilities of proposed method.