Reconstruction of hyperspectral images from RGB images
Pratish Pushparaj, Nishthavan Dahiya, Mayank Dabas · IOP Conference Series Materials Science and Engineering · 2021
Abstract This paper accounts for the problem of construction of hyper-spectral (hs) images from RGB-images, i.e recovery of the whole spectral details/signature from a three-channel RGB image. The dataset used in this paper consists of ‘clean’ images, that are images without noise. There are 450 clean images along with correlative 450 hyperspectral images and all the pages are in PNG (.png) format. We approached this problem using 3 models Convhs_5, Enhanced-ResNet and Dense-HSCNN (D-HSCNN). These models increase in complexity from Convhs_5 to Dense-HSCNN. In the evaluation phase, Convhs_5 achieved average results with not so much clarity, whereas the best model came out to be Enhanced-ResNet with satisfying results, however, due to insufficiency of computational power/resources, we were not able to get the expected results from the Dense-HSCNN model. Whilst having more number of images in the dataset and more computational power can reduce the loss in all of the models furthermore.