Wavelet Burst Accumulation for turbulence mitigation

TL;DR

This paper extends the Fourier burst accumulation method into the wavelet domain, incorporating non-rigid registration and sparsity promotion to improve turbulence mitigation in image sequences.

Contribution

It introduces wavelet domain processing and non-rigid registration to enhance turbulence mitigation, along with sparsity-based weighting approaches.

Findings

Wavelet domain processing improves image reconstruction quality.

Non-rigid registration effectively handles atmospheric turbulence.

Sparsity promotion offers an alternative weighting strategy.

Abstract

In this paper, we investigate the extension of the recently proposed weighted Fourier burst accumulation (FBA) method into the wavelet domain. The purpose of FBA is to reconstruct a clean and sharp image from a sequence of blurred frames. This concept lies in the construction of weights to amplify dominant frequencies in the Fourier spectrum of each frame. The reconstructed image is then obtained by taking the inverse Fourier transform of the average of all processed spectra. In this paper, we first suggest to replace the rigid registration step used in the original algorithm by a non-rigid registration in order to be able to process sequences acquired through atmospheric turbulence. Second, we propose to work in a wavelet domain instead of the Fourier one. This leads us to the construction of two types of algorithms. Finally, we propose an alternative approach to replace the weighting idea by an approach promoting the sparsity in the used space. Several experiments are provided to illustrate the efficiency of the proposed methods.