Spectral Representation via Data-Guided Sparsity for Hyperspectral Image Super-Resolution

Abstract

Hyperspectral imaging is capable of acquiring the rich spectral information of scenes and has great potential for understanding the characteristics of different materials in many applications ranging from remote sensing to medical imaging. However, due to hardware limitations, the existed hyper-/multi-spectral imaging devices usually cannot obtain high spatial resolution. This study aims to generate a high resolution hyperspectral image according to the available low resolution hyperspectral and high resolution RGB images. We propose a novel hyperspectral image superresolution method via non-negative sparse representation of reflectance spectra with a data guided sparsity constraint. The proposed method firstly learns the hyperspectral dictionary from the low resolution hyperspectral image and then transforms it into the RGB one with the camera response function, which is decided by the physical property of the RGB imaging camera. Given the RGB vector and the RGB dictionary, the sparse representation of each pixel in the high resolution image is calculated with the guidance of a sparsity map, which measures pixel material purity. The sparsity map is generated by analyzing the local content similarity of a focused pixel in the available high resolution RGB image and quantifying the spectral mixing degree motivated by the fact that the pixel spectrum of a pure material should have sparse representation of the spectral dictionary. Since the proposed method adaptively adjusts the sparsity in the spectral representation based on the local content of the available high resolution RGB image, it can produce more robust spectral representation for recovering the target high resolution hyperspectral image. Comprehensive experiments on two public hyperspectral datasets and three real remote sensing images validate that the proposed method achieves promising performances compared to the existing state-of-the-art methods.

New technologies for tissue replacement

“…For at least 20 years, the possibility of splitting visible and near-infrared light into its spectral components and then detecting these has made it possible to characterise images with details that would otherwise not be visible (6). This technology, known as hyperspectral imaging (HSI), is based on the possibility of analysing the spectra of an incident light beam after it has been refracted in the tissues, mainly by haemoglobin, S cytochromes, melanin and other chromophores, at a depth that is dependent on the wavelength of the incident light (7).…”

Hyperspectral imaging in wound care: A systematic review

“…In this sense, hyperspectral imaging is the natural extension of the colour (RGB) imaging. Spectrum at each pixel can be considered a spectroscopic input, which can be decomposed, and spectral signatures can be found 42 . There are a few different techniques for acquiring the three‐dimensional (x,y,λ) dataset of a hyperspectral cube (eg, spatial scanning and spectral scanning) 43 .…”