Search Results for author:
Found 8 papers, 4 papers with code
Variational Zero-shot Multispectral Pansharpening
The most challenging issue for this task is that only the to-be-fused LRMS and PAN are available, and the existing deep learning-based methods are unsuitable since they rely on many training pairs.
HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models
Specifically, the reduced image, which has a low spectral dimension, lies in the image field and can be inferred from our improved diffusion model where a new guidance function with total variation (TV) prior is designed to ensure that the reduced image can be well sampled.
Empowering Low-Light Image Enhancer through Customized Learnable Priors
In this work, we propose a paradigm for low-light image enhancement that explores the potential of customized learnable priors to improve the transparency of the deep unfolding paradigm.
Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model
To address these issues, in this work, we propose a low-rank diffusion model for hyperspectral pansharpening by simultaneously leveraging the power of the pre-trained deep diffusion model and better generalization ability of Bayesian methods.
Random Weights Networks Work as Loss Prior Constraint for Image Restoration
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 belief ``Random Weights Networks can Be Acted as Loss Prior Constraint for Image Restoration''.
Learning to adapt unknown noise for hyperspectral image denoising
The weight in this term is used to assess the noise intensity and thus elementwisely adjust the contribution of the observed noisy HSI in a denoising model.
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation
The model-based methods have good generalization ability, while the runtime cannot meet the fast processing requirements of the practical situations due to the large size of an HSI data $ \mathbf{X} \in \mathbb{R}^{MN\times B}$.
Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise
A general approach for handling hyperspectral image (HSI) denoising issue is to impose weights on different HSI pixels to suppress negative influence brought by noisy elements.
