# A Survey on Visual Mamba
## 4 Visual Mamba in Application Fields
### General Visual Mamba
#### 4.1.2 Low-Level Vision  
In the realm of image super-resolution, meet more areas (MMA) [41] stands out as a novel model designed for super-resolution tasks. By building on the ViM block, MMA aims to enhance performance by activating a wider range of areas within images. On this basis, MMA adopts several key strategies, including adding ViM to modules in MetaFormer style, pre-training ViM on larger datasets, and employing complementary attention mechanisms. MMA comprises the following three primary modules: shallow feature extraction, deep feature extraction, and high-quality reconstruction. By leveraging the ViM module, MMA effectively models global information and further expands the activation region through attention mechanisms. Existing restoration backbones often confront a dilemma between global receptive fields and efficient computation, hindering their application in practice, while Mampa has a lot of promise for linear complexity long-range dependency modeling, which can also offer an efficient a way to resolve the above dilemma. MampaIR [57] aimed to address the problem by introducing local enhancement and channel attention mechanisms to enhance the standard Mampa model. The methodology of the model mainly consists of three stages: shallow feature extraction, deep feature extraction, and high-quality image reconstruction. Among them, the deep feature extraction stage utilizes multiple residual state space blocks (RSSBs) for feature extraction, adding a VSS block before the channel attention block designed in previous transformer-based restoration networks. SERPENT [58] has a hierarchical architecture, processing input images in a multi-scale manner, including processing steps such as segmentation, embedding, downsampling, and upsampling, and introduces jump connections to facilitate information flow. Among them, the Serpent block is the main processing unit, consisting of multiple VSS blocks stacked on top of each other. Serpent reduces the computational effort, GPU memory demand, and model size significantly, while preserving good reconstruction quality by combining the benefits of transformers and convolutional networks. VmambalR [32] put forward the OSS module to comprehensively and efficiently model image features from six directions. In addition, the omnidirectional selective scanning mechanism overcomes the unidirectional modeling limitation of SSMs and accomplishes thorough pattern identification and modeling by simulating the three-dimensional visual information flow. UVM-Net [85] refers to a novel single-image defogging network architecture exhibiting effective performance by merging the long-range dependency modeling capacity of SSMs with the local feature extraction of convolutional layers. The method employs an encoder-decoder network architecture, and the critical component is the ViM block, which leverages the long-range modeling capability of SSMs through rolling the feature map over the channel domain. Differently from U-Mampa [84] and Mampa-UNet [51], with long-range dependencies on the non-channel domain, a different feature map dimension is established using the ViM block. Images lose important frequency information under the influence of raindrops, affecting the performance of visual perception and advanced visual tasks. FreqMamba [68] is a novel image de-raining method combining Mampa modeling and frequency analysis techniques to deal with the image de-raining problem. Specifically, FreqMamba contains three branching structures, including spatial Mampa, frequency band Mampa, and Fourier global modeling. Spatial Mampa processes raw image features to extract details and correlations within the image. Frequency band Mampa employs wavelet packet transform (WPT) to decompose the input features into spectral features in different frequency bands and scan them over the frequency dimension. Fourier modeling, i.e., processing an input using Fourier transform, captures the global degradation patterns that can affect an image.