# A Survey on Visual Mamba
## 4 Visual Mamba in Application Fields
### General Visual Mamba
#### 4.1.2 Low-Level Vision
Numerous tests have shown that FreqMamba works better than current state-of-the-art techniques in terms of both visual and quantitative aspects. Image deblurring is a traditional issue in low-level computer vision, with the goal of restoring crisp, high-quality images from hazy input photographs. ALGNet [107] is an efficient image deblurring network utilizing selective state-space models (SSM) to aggregate rich and accurate features. The network consists of multiple ALGBlocks, each of which contains a CLGF module that captures local and global features and a feature aggregation module FA. The CLGF module captures long-range dependent features using a SSM and employs a channel-attention mechanism to lower local pixel forgetting and channel redundancy. Through weight calibration, the FA module highlights the significance of local features in recovery. The efficiency of Mampa makes a significant contribution to mitigating the high computational complexity associated with training generation tasks. To address the change in generating long and diverse sequences with low latency, MambaTalk [74] implements a two-stage modeling strategy with discrete motion priors to improve the quality of gestures and employs a mamba block to enhance gesture diversity and rhythm through multimodal integration. Motion Mamba [34] was introduced to construct a motion generation model based on Mamba, leveraging an efficient hardware-aware design. Motion Mamba consists of the following two main components: a hierarchical temporal Mamba (HTM) block for temporal data handling; and for analyzing latent postures, a bidirectional spatial Mamba (BSM) block. To maintain motion consistency across frames, the HTM block employs several separate SSM modules within a U-Net architecture that is balanced. Meanwhile, the BSM block enhances the accuracy of motion generation within a temporal frame by processing latent poses bidirectionally. Diffusion state space odeis (DiS) [88] substitute the conventional U-Net backbone in diffusion models with SSM. All inputs are taken into account by this system, including time, conditions, and tokens and noisy image patches. To address the oversight of spatial continuity in the scanning scheme of existing Mamba-based vision methods, Zigzag Mamba [31] was introduced as a straightforward, plug-and-play solution inspired by DiT-style approaches. Essentially, it retains the scanning scheme of plain Mamba but expands it from four to eight schemes by incorporating mirror flipping schemes, as displayed in Figure 4f. Then, Zigzag Mamba was integrated using the framework of the stochastic interpolant, forming ZigMa, to investigate the diffusion model's scalability using high-resolution visual datasets. GAMBA [111] introduces a sequential network based on Mamba, allowing for linear sequence length scalability and context-dependent reasoning. This architecture accommodates many Gaussians for the 3D Gaussian splatting process. To deal with the issue of the increase in quadratic memory consumption with sequence length in traditional attention-based video generative diffusion models, an SSM-based diffusion model [89] was introduced for generating longer video sequences. Similarly to ViS4 mer [80], the SSM-based diffusion model re-imagines the attention modules within the conventional temporal layers of video diffusion models. It can replace them with a ViM block designed to record the temporal changes of video data and an MLP to improve model performance. Moreover, this innovative approach significantly mitigates memory consumption for extended sequences. In 3D vision tasks, the irregularity and sparsity of point cloud data present considerable hurdles. While transformers exhibit promise in exploring point cloud data, due to their strong global information modeling capability, but their computational complexity escalates significantly as the input length increases. This limitation restricts their applicability, particularly in long sequence models.