LaMamba: Linear Attention Mamba for Hyperspectral Image Denoising

Hyperspectral images (HSIs) are often affected by noise originating from both internal imaging mechanisms and external environmental factors. Therefore, denoising serves as a crucial preprocessing step for HSIs. In real-world scenarios, HSI denoising is particularly challenging due to the complex and band-dependent nature of noise. Current Mamba-based models, although capable of sequential modeling, are highly sensitive to input spectral order and still suffer from information loss over relatively long sequences, potentially leading to local over-sharpening and spectral distortion. To address these issues, we propose a novel linear attention Mamba (LaMamba) for HSI denoising. To capture the intrinsic nature of HSI, a 3D selective scan mechanism is designed to convert the input HSI into spectral-spatial continuous sequences using six bidirectional scan orders. Additionally, a linear attention state space model is proposed to capture long-range correlation. Experimental results on both synthetic and real-world hyperspectral datasets demonstrate that our model significantly outperforms other advanced methods in reconstructing spectral fidelity and spatial visual effect. The code is released at https://github.com/PuhongDuan/LaMamba.