The field of image and signal processing is witnessing significant advancements with the development of Mamba-based models. These models have shown exceptional capabilities in modeling long-range dependencies, making them particularly useful for tasks such as image super-resolution, medical anomaly detection, and point cloud learning. The integration of Mamba with other architectures, such as CNNs and transformers, has led to the creation of hybrid models that leverage the strengths of each component. Notably, the use of frequency-based learning and reconstruction has improved the accuracy and efficiency of medical image segmentation. Furthermore, the application of Mamba-based models to tasks such as video camouflaged object detection and multimodal segmentation has demonstrated their versatility and potential for real-world applications. Some noteworthy papers in this area include GPSMamba, which achieves state-of-the-art performance in infrared image super-resolution, and Vcamba, which proposes a novel visual camouflage Mamba based on spatio-frequency motion perception for efficient and accurate video camouflaged object detection. Additionally, FaRMamba and MambaVesselNet++ have shown impressive results in medical image segmentation, highlighting the potential of Mamba-based models in this field.