LOW-RANK REPRESENTATION LEARNING FOR HYPERSPECTRAL IMAGE SUPER-RESOLUTION

Hyperspectral images (HSI) face difficulties in achieving high resolution (HR) due to the limitations of the imaging devices. To address this challenge, image super-resolution (SR) techniques have been introduced to reconstruct HR images from low-resolution (LR) inputs. Several deep learning-based image SR algorithms have demonstrated impressive performance. However, these methods face challenges in effectively leveraging the spectral and spatial correlations, as HSI exhibit spectral low-rank characteristics and spatial self-similarity. In this paper, we propose learning a low-rank representation (LRR) that captures the correlations between spectral bands. Specifically, the learned LRR can be incorporated into the attention mechanism, which simultaneously leverages the spatial self-similarity of HSI. The LRR reveals the underlying structure of HSI, enhancing the model’s ability to capture fine details. Moreover, we employ a 2-D/3-D hybrid convolution that leverages spectral information and enhances the learning of spatial features. Experimental results demonstrate that our method can reconstruct the SR images with more accurate edges and finer details.