TL;DR
This paper presents a new turbulence stabilization algorithm that removes atmospheric distortions from image sequences using a variational approach, Bregman iterations, and explores the impact of different regularization terms.
Contribution
It introduces an analysis of how the choice of regularization affects turbulence stabilization and tests various regularizers in the proposed variational framework.
Findings
Regularization choice significantly influences stabilization quality
Certain regularizers outperform others in preserving image details
The method effectively reduces atmospheric distortions in tested sequences
Abstract
We recently developed a new approach to get a stabilized image from a sequence of frames acquired through atmospheric turbulence. The goal of this algorihtm is to remove the geometric distortions due by the atmosphere movements. This method is based on a variational formulation and is efficiently solved by the use of Bregman iterations and the operator splitting method. In this paper we propose to study the influence of the choice of the regularizing term in the model. Then we proposed to experiment some of the most used regularization constraints available in the litterature.
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