70 citations · 95 across the 7 of their papers we have counts for
7 papers
Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance
Yingkai Zhang, Zeqiang Lai, Tao Zhang +2
Hyperspectral images super-resolution aims to improve the spatial resolution, yet its performance is often limited at high-resolution ratios. The recent adoption of high-resolution…
Denoising Diffusion Semantic Segmentation with Mask Prior Modeling
Zeqiang Lai, Yuchen Duan, Jifeng Dai +5
The evolution of semantic segmentation has long been dominated by learning more discriminative image representations for classifying each pixel. Despite the prominent advancements,…
Hybrid Spectral Denoising Transformer with Guided Attention
Zeqiang Lai, Chenggang Yan, Ying Fu
In this paper, we present a Hybrid Spectral Denoising Transformer (HSDT) for hyperspectral image denoising. Challenges in adapting transformer for HSI arise from the capabilities t…
Hyperspectral Image Super Resolution with Real Unaligned RGB Guidance
Zeqiang Lai, Ying Fu, Jun Zhang
Fusion-based hyperspectral image (HSI) super-resolution has become increasingly prevalent for its capability to integrate high-frequency spatial information from the paired high-re…
Mixed Attention Network for Hyperspectral Image Denoising
Zeqiang Lai, Ying Fu
Hyperspectral image denoising is unique for the highly similar and correlated spectral information that should be properly considered. However, existing methods show limitations in…
Improved Quasi-Recurrent Neural Network for Hyperspectral Image Denoising
Zeqiang Lai, Ying Fu
Hyperspectral image is unique and useful for its abundant spectral bands, but it subsequently requires extra elaborated treatments of the spatial-spectral correlation as well as th…
Deep Plug-and-Play Prior for Hyperspectral Image Restoration
Zeqiang Lai, Kaixuan Wei, Ying Fu
Deep-learning-based hyperspectral image (HSI) restoration methods have gained great popularity for their remarkable performance but often demand expensive network retraining whenev…