Ssumamba: Spatial-spectral selective state space model for hyperspectral image denoising
Denoising is a crucial preprocessing step for hyperspectral images (HSIs) due to noise
arising from intraimaging mechanisms and environmental factors. Long-range spatial …
arising from intraimaging mechanisms and environmental factors. Long-range spatial …
Transformers in Material Science: Roles, Challenges, and Future Scope
N Rane - Challenges, and Future Scope (March 26, 2023), 2023 - papers.ssrn.com
This study explores the diverse applications, challenges, and future prospects of employing
vision transformers in various material science domains, including biomaterials, ceramic …
vision transformers in various material science domains, including biomaterials, ceramic …
Region-Aware Sequence-to-Sequence Learning for Hyperspectral Denoising
Proper spectral modeling within hyperspectral image (HSI) is critical yet highly challenging
for HSI denoising. In contrast to existing methods that struggle between effectiveness and …
for HSI denoising. In contrast to existing methods that struggle between effectiveness and …
Prompthsi: Universal hyperspectral image restoration framework for composite degradation
Recent developments in All-in-One (AiO) RGB image restoration and prompt learning have
enabled the representation of distinct degradations through prompts, allowing degraded …
enabled the representation of distinct degradations through prompts, allowing degraded …
Hsidmamba: Exploring bidirectional state-space models for hyperspectral denoising
Effectively discerning spatial-spectral dependencies in HSI denoising is crucial, but
prevailing methods using convolution or transformers still face computational efficiency …
prevailing methods using convolution or transformers still face computational efficiency …
Bridging fourier and spatial-spectral domains for hyperspectral image denoising
Remarkable progresses have been made in hyperspectral image (HSI) denoising. However,
the majority of existing methods are predominantly confined to the spatial-spectral domain …
the majority of existing methods are predominantly confined to the spatial-spectral domain …
Latent Diffusion Enhanced Rectangle Transformer for Hyperspectral Image Restoration
The restoration of hyperspectral image (HSI) plays a pivotal role in subsequent
hyperspectral image applications. Despite the remarkable capabilities of deep learning …
hyperspectral image applications. Despite the remarkable capabilities of deep learning …
Hierarchical Separable Video Transformer for Snapshot Compressive Imaging
Transformers have achieved the state-of-the-art performance on solving the inverse problem
of Snapshot Compressive Imaging (SCI) for video, whose ill-posedness is rooted in the …
of Snapshot Compressive Imaging (SCI) for video, whose ill-posedness is rooted in the …
Exploring high-order correlation for hyperspectral image denoising with hypergraph convolutional network
High-order correlation is an important property of hyperspectral images (HSIs) and has been
widely investigated in model-based HSI denoising. However, the existing deep learning …
widely investigated in model-based HSI denoising. However, the existing deep learning …
Hybrid Spatial-spectral Neural Network for Hyperspectral Image Denoising
H Liang, K Li, X Tian - arxiv preprint arxiv:2406.08782, 2024 - arxiv.org
Hyperspectral image (HSI) denoising is an essential procedure for HSI applications.
Unfortunately, the existing Transformer-based methods mainly focus on non-local modeling …
Unfortunately, the existing Transformer-based methods mainly focus on non-local modeling …