The recent research in atmospheric turbulence stabilization and non-uniformity correction in imaging has seen significant advancements. Innovations in variational models and optimization techniques, particularly through Bregman Iteration and operator splitting methods, have enabled more efficient and generalized solutions for turbulence-induced geometric distortions. Additionally, analytical approaches leveraging the Fried kernel and framelet-based deconvolution have shown promise in deblurring long-range imaging affected by atmospheric turbulence. In the realm of infrared imaging, novel single image non-uniformity correction algorithms have been developed, addressing the inherent noise issues without the need for complex calibration or motion compensation. These methods, characterized by their simplicity and real-time applicability, represent a notable leap forward in the field.

Noteworthy papers include one that introduces a variational model for atmospheric turbulence stabilization, efficiently solved by Bregman Iteration and operator splitting, and another that presents a Fried kernel-based deconvolution method for atmospheric turbulence deblurring, which simplifies implementation while achieving high-quality results.