Bidirectional-Aware Network Combining Transformer and Mamba for Hyperspectral Image Denoising
Cheng Chen, Jie Li, Xinxin Liu, Qiangqiang Yuan, Liangpei Zhang · IEEE Transactions on Geoscience and Remote Sensing · 2025
Hyperspectral images (HSIs) often suffer from various noises, such as Gaussian noise, stripe noise, impulse noise, and deadlines due to the influence of sensors and external environments. These noises significantly degrade the quality of HSI and hinder subsequent applications. While most current transformer-based methods can effectively remove certain types of noise, they struggle with wide stripe noise. In addition, transformers are typically applied within local windows due to the limitation of computational complexity. Although windowshifting operations enhance the interaction between windows to a certain extent, this interaction remains insufficient for comprehensive global modeling. In light of these limitations, we propose a Bidirectional-aware network combining Transformer and Mamba (BTMnet), which consists of Bidirectional Long-Short Distance Attention (BLSDA) and Channel-Split Mamba (CSM). To better remove wide stripe noise, BLSDA is designed with two rectangular windows adapted to wide stripes in both vertical and horizontal directions, utilizing transformers to compute attention relationships within windows and across different windows. To further integrate global information and enhance the interaction of features between adjacent windows, CSM extracts global features by scanning in four directions across different feature channels. In BLSDA, we applied bidirectional windows in vertical and horizontal directions, and in CSM, we conducted bidirectional scanning in vertical and horizontal directions. The combination of these techniques allows for the simultaneous extraction of bidirectional features from HSI. By evaluating the metrics and visualization, the experimental results on simulated and real experiments prove that our method can achieve better results.