Spectral Grouping Driven Hyperspectral Super-Resolution
Sadia Hussain, Brejesh Lall · 2023
Convolutional neural networks have proven to be proficient when extracting low-level concepts in an image. With the wonderful performance of transformers in exploiting the long-range correlations in an image, many methods have been explored where one exploit benefits of both the architectures. Therefore, in order to strengthen our network we add an important feature to transformers wherein single image super-resolution (SISR) is exploited using band grouping leveraging a simple CNN architecture. This paper aims to train a set of simple residual modelling architectures and then integrate them into a transformer architecture to solve super-resolution problem in HSI. We take a step forward to analyse how to adapt swinIR to fully exploit the information derived from band grouping for efficient SISR.