Open Access

ARTICLE

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

Mahmood Ashraf^1,2, Nuha Zamzami³, Shtwai Alsubai⁴, Raed Alharthi⁵, Muhammad Umer^6,*, Yunyoung Nam⁷, Yongwon Cho^7,*

1 Department of Computer Science and Information Technology, University of Kamalia, Kamalia, Pakistan
2 Department of Communication and Cyber Security, Bahuddin Zakariya University, Multan, Pakistan
3 Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
4 Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
5 Department of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Riyadh, Saudi Arabia
6 Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
7 Department of Computer Science and Engineering, Soonchunhyang University, Asan, Republic of Korea

* Corresponding Authors: Muhammad Umer. Email: ; Yongwon Cho. Email:

(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)

Computer Modeling in Engineering & Sciences 2026, 147(3), 41 https://doi.org/10.32604/cmes.2026.078738

Received 07 January 2026; Accepted 27 April 2026; Issue published 30 June 2026

Abstract

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.

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Keywords

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