OSSMDNet: An Omni-Selective Scanning Mechanism for a Remote Sensing Image Denoising Network Based on the State-Space Model
Na Deng, Jie Han, Haiyong Ding, Dongsheng Liu, Zhichao Zhang, Wenping Song, Xudong Tong · Remote Sensing · 2025
Remote sensing images often degrade during acquisition due to various environmental factors, leading to noise contamination and loss of texture details. Existing methods based on convolutional neural networks (CNNs) are limited by their local receptive fields, making it difficult to effectively model long-range dependencies. Although Transformers possess global modeling capabilities, they face high computational costs and poor scalability in high-resolution remote sensing images. To address these challenges, this paper proposes an efficient remote sensing image denoising network—OSSMDNet—based on the Mamba network and incorporating an omni-directional selective scanning mechanism (OSSM). Its advantages include (1) introducing a multi-directional state-space modeling mechanism to enhance spatial structure perception capabilities and mitigate the limitations of traditional unidirectional modeling; (2) OSSMDNet is designed based on the Mamba architecture, achieving efficient fusion of global context and local details while maintaining linear computational complexity. On multiple remote sensing and natural image denoising datasets such as CBSD68 and DOTA, OSSMDNet significantly outperforms existing CNN-, Transformer-, and Mamba-based methods in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, with PSNR and SSIM values 0.14 dB and 0.0033 higher than the most iconic Mamba baseline method, respectively. This demonstrates that the proposed OSSMDNet achieves an excellent balance between accuracy and efficiency.