Effectively discerning spatial-spectral dependencies in HSI denoising is crucial, but prevailing methods using convolution or transformers still face computational efficiency limitations. Recently, the emerging Selective State Space Model(Mamba) has risen with its nearly linear computational complexity in processing natural language sequences, which inspired us to explore its potential in handling long spectral sequences. In this paper, we propose HSIDMamba(HSDM), tailored to exploit the linear complexity for effectively capturing spatial-spectral dependencies in HSI denoising. In particular, HSDM comprises multiple Hyperspectral Continuous Scan Blocks, incorporating BCSM(Bidirectional Continuous Scanning Mechanism), scale residual, and spectral attention mechanisms to enhance the capture of long-range and local spatial-spectral information. BCSM strengthens spatial-spectral interactions by linking forward and backward scans and enhancing information from eight directions through SSM, significantly enhancing the perceptual capability of HSDM and improving denoising performance more effectively. Extensive evaluations against HSI denoising benchmarks validate the superior performance of HSDM, achieving state-of-the-art results in performance and surpassing the efficiency of the latest transformer architectures by $30\%$.

翻译：在高光谱图像去噪中，有效识别空间-光谱依赖性至关重要，但现有基于卷积或Transformer的方法仍面临计算效率的局限。近期，新兴的选择性状态空间模型（Mamba）因其在处理自然语言序列时近乎线性的计算复杂度而备受关注，这启发我们探索其在处理长光谱序列方面的潜力。本文提出HSIDMamba（HSDM），旨在利用线性复杂度有效捕捉高光谱去噪中的空间-光谱依赖性。具体而言，HSDM由多个高光谱连续扫描块组成，结合双向连续扫描机制（BCSM）、尺度残差和光谱注意力机制，以增强对长距离和局部空间-光谱信息的捕获。BCSM通过连接前向和后向扫描，并借助状态空间模型从八个方向增强信息来强化空间-光谱交互，显著提升HSDM的感知能力，从而更有效地改善去噪性能。在高光谱去噪基准上的大量评估验证了HSDM的优越性能，其在性能上达到了最先进水平，且效率比最新的Transformer架构高出30%。