Feature Fusion-Guided Network With Sparse Prior Constraints for Unsupervised Hyperspectral Image Quality Improvement
Feiwang Yuan, Yong Chen, Wei He, Jinshan Zeng
Abstract
Due to imaging hardware limitations and atmospheric interference, hyperspectral image (HSI) often suffers from low spatial resolution or mixed noise degradation. HSI fusion and denoising are two key strategies to improve HSI quality. Traditional model-based methods rely on data-specific manual priors, while supervised deep learning methods are typically developed specifically for a single task and require a large number of training datasets. To address these limitations, we propose a novel unsupervised feature fusion-guided network (UFFGNet) as a general prior that effectively leverages multi-scale semantic features from a guidance image while incorporating sparse prior constraints to suppress outliers. Specifically, UFFGNet comprises a deep feature extraction network to capture multiscale semantic features from a guidance image, and an attentionbased feature generation network that generates an output image from random noise. These two networks are connected by a feature refinement module to embed the refined features from the feature aggregation module into the generation network. Furthermore, the sparse prior constraint is incorporated to model sparse noise (including impulse noise, stripe artifacts, and deadlines) in HSI, thereby improving the robustness of UFFGNet. The proposed network optimizes network parameters in an unsupervised manner without external additional training data, using the fidelity term in the degradation model as a loss function to learn the prior information of the original image. Extensive experiments demonstrate that the proposed UFFGNet outperforms state-of-the-art methods in both HSI fusion and denoising tasks, significantly improving HSI quality.