Distortion-driven Turbulence Effect Removal using Variational Model

TL;DR

This paper introduces a novel variational model combined with distortion-driven kernel regression to effectively remove geometric distortion and space-time-varying blur from turbulent atmospheric images, improving detail recovery.

Contribution

It presents a new variational framework and a fast algorithm for joint distortion correction and deblurring, outperforming existing methods.

Findings

Effective distortion and blur removal demonstrated through experiments

Improved detail recovery compared to state-of-the-art methods

Efficient algorithm without PDEs

Abstract

It remains a challenge to simultaneously remove geometric distortion and space-time-varying blur in frames captured through a turbulent atmospheric medium. To solve, or at least reduce these effects, we propose a new scheme to recover a latent image from observed frames by integrating a new variational model and distortion-driven spatial-temporal kernel regression. The proposed scheme first constructs a high-quality reference image from the observed frames using low-rank decomposition. Then, to generate an improved registered sequence, the reference image is iteratively optimized using a variational model containing a new spatial-temporal regularization. The proposed fast algorithm efficiently solves this model without the use of partial differential equations (PDEs). Next, to reduce blur variation, distortion-driven spatial-temporal kernel regression is carried out to fuse the registered sequence into one image by introducing the concept of the near-stationary patch. Applying a blind deconvolution algorithm to the fused image produces the final output. Extensive experimental testing shows, both qualitatively and quantitatively, that the proposed method can effectively alleviate distortion and blur and recover details of the original scene compared to state-of-the-art methods.