The field of computer vision is witnessing significant advancements in image restoration and object tracking, driven by the development of efficient state space models. These models have demonstrated excellence in modeling long-range dependencies with linear complexity, a crucial advantage for tasks such as image restoration and object tracking. Researchers are exploring innovative approaches to address the challenges of computational complexity, local pixel forgetting, and temporal inconsistency in existing methods. Noteworthy papers in this area include EAMamba, which achieves a significant reduction in FLOPs while maintaining favorable performance, and Laplace-Mamba, which integrates Laplace frequency prior with a hybrid Mamba-CNN architecture for efficient image dehazing. TrackingMiM is also noteworthy for its Mamba-in-Mamba architecture, which enables real-time UAV object tracking with state-of-the-art precision and speed.