2023/05/06 by Haijin Zeng, Jiezhang Cao, Zeng, Haijin +11 · 4 citations
Computer Science · Engineering · #Advanced Image Fusion Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #Remote-Sensing Image Classification #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2305.04047
openalex publication_date 2023/05/06 · openalex created_date 2023/05/10 · openalex updated_date 2026/07/28
Hyperspectral imaging (HI) has emerged as a powerful tool in diverse fields such as medical diagnosis, industrial inspection, and agriculture, owing to its ability to detect subtle differences in physical properties through high spectral resolution. However, hyperspectral images (HSIs) are often quite noisy because of narrow band spectral filtering. To reduce the noise in HSI data cubes, both model-driven and learning-based denoising algorithms have been proposed. However, model-based approaches rely on hand-crafted priors and hyperparameters, while learning-based methods are incapable of estimating the inherent degradation patterns and noise distributions in the imaging procedure, which could inform supervised learning. Secondly, learning-based algorithms predominantly rely on CNN and fail to capture long-range dependencies, resulting in limited interpretability. This paper proposes a Degradation-Noise-Aware Unfolding Network (DNA-Net) that addresses these issues. Firstly, DNA-Net models sparse noise, Gaussian noise, and explicitly represent image prior using transformer. Then the model is unfolded into an end-to-end network, the hyperparameters within the model are estimated from the noisy HSI and degradation model and utilizes them to control each iteration. Additionally, we introduce a novel U-Shaped Local-Non-local-Spectral Transformer (U-LNSA) that captures spectral correlation, local contents, and non-local dependencies simultaneously. By integrating U-LNSA into DNA-Net, we present the first Transformer-based deep unfolding HSI denoising method. Experimental results show that DNA-Net outperforms state-of-the-art methods, and the modeling of noise distributions helps in cases with heavy noise.