Computationally Efficient Gradient-Aware Hyperspectral Image Denoising Using Center-Difference Convolutional Networks

Hyperspectral image (HSI) denoising is a crucial preprocessing step that significantly enhances the performance of downstream applications, such as object detection and classification. Whereas deep neural networks have achieved remarkable performance in HSI denoising, many existing models rely mostly on vanilla convolutions, which often fail to capture fine-grained noise patterns and structural details in real-time HSIs. To address these limitations, we propose a novel Center-Difference Convolutional Network (CDCN) designed to effectively suppress various noise types while preserving the inherent structure of HSIs. By leveraging center-difference convolution (CDC), our model captures both gradient and intensity information in the spatial domain, enabling better discrimination of subtle noise characteristics. The CDCN architecture processes 3D HSI cubes through separable 3D convolutions, efficiently extracting spatial-spectral features with minimal computational overhead. Additionally, a spatial-spectral attention mechanism is integrated to further refine feature representation. We evaluate the proposed method on one simulated dataset (Kennedy Space Center) and two real-world datasets (Pavia Center and Houston-2018). Experimental results demonstrate that CDCN consistently outperforms existing state-of-the-art approaches, achieving superior denoising performance while maintaining spectral-spatial information. Ablation studies also validate the effectiveness of CDC and attention mechanisms in enhancing denoising capability over standard convolutional baselines.