CNN-Enhanced graph attention network for hyperspectral image super-resolution using non-local self-similarity

Liu, Cong; Dong, Yaxin

doi:10.1080/01431161.2022.2121188

Cited by 3 publications

(2 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To validate the effectiveness of our model, we conducted a comprehensive comparison with various state-of-the-art SISR methods. We used standard bicubic interpolation as a baseline and evaluated our results against six SOTA SISR methods: EDSR (Lim et al 2017), GDRRN (Li et al 2018), SSPSR (Jiang et al 2020), MCNet (Li, Wang, and Li 2020), CEGATSR (Liu and Dong 2022), and GELIN . This comparison was conducted on three different datasets at three different scales.…”

Section: Results and Comparison With Sotamentioning

confidence: 99%

“…Building on this approach, proposed the Group-based Embedding Learning and Integration Network (GELIN), which effectively utilizes information from neighboring spectral bands. Furthermore, many other research efforts have been based on the spectral grouping strategy, as demonstrated in (Liu, Fan, and Zhang 2022), (Liu and Dong 2022), (Liu et al 2022a) and . Additionally, some researchers have utilized Transformer-based architectures to learn the complex relationships between spectral and spatial information, as seen in (Gao et al 2021), (Hu et al 2022) and (Liu et al 2022d).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Hyperspectral Images via Diffusion Model and Group-Autoencoder Super-resolution Network

Wang,

Li,

Zhang

et al. 2024

AAAI

View full text Add to dashboard Cite

Existing hyperspectral image (HSI) super-resolution (SR) methods struggle to effectively capture the complex spectral-spatial relationships and low-level details, while diffusion models represent a promising generative model known for their exceptional performance in modeling complex relations and learning high and low-level visual features. The direct application of diffusion models to HSI SR is hampered by challenges such as difficulties in model convergence and protracted inference time. In this work, we introduce a novel Group-Autoencoder (GAE) framework that synergistically combines with the diffusion model to construct a highly effective HSI SR model (DMGASR). Our proposed GAE framework encodes high-dimensional HSI data into low-dimensional latent space where the diffusion model works, thereby alleviating the difficulty of training the diffusion model while maintaining band correlation and considerably reducing inference time. Experimental results on both natural and remote sensing hyperspectral datasets demonstrate that the proposed method is superior to other state-of-the-art methods both visually and metrically.

show abstract