Enhanced Hyperspectral Image Super-Resolution Via RGB Fusion and TV-TV Minimization

Marija Vella, Bowen Zhang, Wei Chen, João F. C. Mota

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
11 Downloads (Pure)


Hyperspectral (HS) images contain detailed spectral information that has proven crucial in applications like remote sensing, surveillance, and astronomy. However, because of hardware limitations of HS cameras, the captured images have low spatial resolution. To improve them, the low-resolution hyperspectral images are fused with conventional high-resolution RGB images via a technique known as fusion based HS image super-resolution. Currently, the best performance in this task is achieved by deep learning (DL) methods. Such methods, however, cannot guarantee that the input measurements are satisfied in the recovered image, since the learned parameters by the network are applied to every test image. Conversely, model-based algorithms can typically guarantee such measurement consistency. Inspired by these observations, we propose a framework that integrates learning and model based methods. Experimental results show that our method produces images of superior spatial and spectral resolution compared to the current leading methods, whether model-or DL-based.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing (ICIP)
Number of pages5
ISBN (Electronic)9781665441155
Publication statusPublished - 23 Aug 2021
Event28th IEEE International Conference on Image Processing 2021 - Anchorage, United States
Duration: 19 Sep 202122 Sep 2021


Conference28th IEEE International Conference on Image Processing 2021
Abbreviated title2021 IEEE ICIP
Country/TerritoryUnited States
Internet address


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