76 citations · 78 across the 8 of their papers we have counts for
6 papers · 1 filter
HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models
Li Pang, Xiangyu Rui, Long Cui +3
Hyperspectral image (HSI) restoration aims at recovering clean images from degraded observations and plays a vital role in downstream tasks. Existing model-based methods have limit…
Empowering Low-Light Image Enhancer through Customized Learnable Priors
Naishan Zheng, Man Zhou, Yanmeng Dong +4
Deep neural networks have achieved remarkable progress in enhancing low-light images by improving their brightness and eliminating noise. However, most existing methods construct e…
HIDFlowNet: A Flow-Based Deep Network for Hyperspectral Image Denoising
Qizhou Wang, Li Pang, Xiangyong Cao +2
Hyperspectral image (HSI) denoising is essentially ill-posed since a noisy HSI can be degraded from multiple clean HSIs. However, existing deep learning (DL)-based approaches only…
Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model
Xiangyu Rui, Xiangyong Cao, Li Pang +3
Hyperspectral pansharpening is a process of merging a high-resolution panchromatic (PAN) image and a low-resolution hyperspectral (LRHS) image to create a single high-resolution hy…
Random Weights Networks Work as Loss Prior Constraint for Image Restoration
Man Zhou, Naishan Zheng, Jie Huang +5
In this paper, orthogonal to the existing data and model studies, we instead resort our efforts to investigate the potential of loss function in a new perspective and present our b…
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation
Jiangjun Peng, Hailin Wang, Xiangyong Cao +3
Mining structural priors in data is a widely recognized technique for hyperspectral image (HSI) denoising tasks, whose typical ways include model-based methods and data-based metho…