Super-Resolution for Spectral Image

Spectral images can quantitatively analyze the spatial and material attributes of objects, which greatly improves the ability of object classification and recognition. However, in the process of obtaining image data, due to the influence of many uncertain factors, such as sensor system and external conditions, the quality of spectral image is reduced, which is not conducive to the effective discrimination of objects. Super-resolution is one of the important techniques to improve image quality for low perception images with insufficient details and noise. Traditional methods mainly focus on the extraction of spatial information and fail to make full use of the potential information between spectra. As a result, the representation ability of the model is weak and the specific details of the image cannot be recovered effectively. To address these issues, this chapter focuses on the scientific problem of spatial-spectral dependence analysis and modeling to mine the internal relationship between multidomain data, so that the details of the reconstructed image should be as clear as possible.