The recent developments in the research area indicate a strong focus on advancing state estimation techniques, particularly in the context of dynamic systems and high-resolution imaging. Innovations in Kalman filtering, such as the invariant Kalman filter and affine EKF, are addressing consistency and robustness issues, paving the way for more accurate and reliable state estimation in various applications. In the realm of imaging, there is a notable shift towards ultra-high-definition (UHD) and high dynamic range (HDR) imaging, with novel methods leveraging advanced learning paradigms and physically-based models to achieve real-time, high-quality image fusion and generation. These advancements are not only enhancing visual fidelity but also broadening the applicability of HDR imaging in dynamic scenes and resource-constrained devices. Notably, the integration of event cameras with traditional RGB cameras is proving to be a promising approach for capturing high-dynamic-range images in challenging, fast-moving environments. Overall, the field is progressing towards more sophisticated, real-time, and physically accurate solutions that bridge the gap between theoretical advancements and practical applications.