Hyperspectral images reconstruction based super-pixel mapping using cross-channel sparse model

Hyperspectral images (HSIs) provide abundant information to solve various kinds of problems like object identification and classification. However, HSIs often inevitably suffer many factors from various resources [1], such as imperfect imaging optics, sensor noise, and atmospheric effects, which degrade the acquired image quality [2]. Thus, HSI image super resolution reconstruction, used to achieve sub-pixel mapping, is an active research topic due to its effectiveness in improving the resolution of hyperspectral image. In the paper, a HSI super-resolution method, in which the different dictionaries are learnt for different bands and sparse structure from wavelength range with high correlation is regarded with similar sparse coefficients , is proposed to obtain the high-resolution image.