The current research in atmospheric turbulence mitigation is witnessing a shift towards leveraging advanced neural network architectures and innovative signal processing techniques. Researchers are increasingly focusing on developing end-to-end neural networks that can directly address the blur caused by atmospheric turbulence, eliminating the need for intermediate stabilization steps. This approach not only simplifies the process but also shows promise in improving the quality of image and video reconstruction. Additionally, there is a growing interest in extending traditional 2D image processing techniques to 3D vector fields, particularly through the use of curvelet spaces, to better characterize and separate motion due to turbulence from actual object movement. This geometric approach is proving effective in detecting moving objects through turbulent media. Furthermore, the field is progressing towards the creation of standardized datasets, such as the Open Turbulent Image Set (OTIS), which will facilitate objective evaluations and comparisons of different turbulence mitigation algorithms. These developments collectively aim to enhance the robustness and accuracy of atmospheric turbulence mitigation techniques, paving the way for more reliable long-distance imaging applications.

Noteworthy papers include one that introduces an end-to-end neural network for turbulence mitigation, demonstrating a significant simplification in the process. Another paper extends 2D decomposition algorithms to 3D vector fields using curvelet spaces, effectively distinguishing between turbulence and object motion.