97 citations · 290 across the 24 of their papers we have counts for
6 papers · 1 filter
Pixel Adaptive Deep Unfolding Transformer for Hyperspectral Image Reconstruction
Miaoyu Li, Ying Fu, Ji Liu +1
Hyperspectral Image (HSI) reconstruction has made gratifying progress with the deep unfolding framework by formulating the problem into a data module and a prior module. Neverthele…
Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising
Miaoyu Li, Ji Liu, Ying Fu +2
Denoising is a crucial step for hyperspectral image (HSI) applications. Though witnessing the great power of deep learning, existing HSI denoising methods suffer from limitations i…
LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising
Zichun Wang, Ying Fu, Ji Liu +1
Despite the significant results on synthetic noise under simplified assumptions, most self-supervised denoising methods fail under real noise due to the strong spatial noise correl…
Instance Segmentation in the Dark
Linwei Chen, Ying Fu, Kaixuan Wei +2
Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments. I…
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…
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…