A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging
This review addresses the problem of expensive hyperspectral imaging by summarizing alternative methods for researchers, but it is incremental as it compiles existing work without introducing new techniques.
The paper surveys computational methods for reconstructing hyperspectral images from RGB images, finding that data-driven deep learning approaches generally outperform prior-based methods in accuracy and quality, though at lower speeds.
Hyperspectral imaging enables versatile applications due to its competence in capturing abundant spatial and spectral information, which are crucial for identifying substances. However, the devices for acquiring hyperspectral images are expensive and complicated. Therefore, many alternative spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from lower-cost, more available RGB images. We present a thorough investigation of these state-of-the-art spectral reconstruction methods from the widespread RGB images. A systematic study and comparison of more than 25 methods has revealed that most of the data-driven deep learning methods are superior to prior-based methods in terms of reconstruction accuracy and quality despite lower speeds. This comprehensive review can serve as a fruitful reference source for peer researchers, thus further inspiring future development directions in related domains.