用小波域稀疏性提升湍流模糊视频的清晰重建效果
Wavelet Burst Accumulation for turbulence mitigation
- 改用非刚性配准处理大气湍流导致的帧间扭曲
- 在小波域构建加权算法，比傅里叶域更适应湍流噪声
- 引入稀疏性约束替代加权，提升重建图像细节
本文研究将近期提出的加权傅里叶突发累积（FBA）方法拓展至小波域。FBA旨在从一系列模糊帧中重建清晰锐利的图像，其核心是为每帧傅里叶谱中的主导频率构造权重，再通过对处理后谱的平均进行逆傅里叶变换得到重建图像。本文首先建议用非刚性配准替代原算法中的刚性配准，以处理大气湍流导致的序列。其次提出在小波域而非傅里叶域进行处理，由此衍生出两类新算法。最后提出一种替代加权思想的方法，通过促进所用空间的稀疏性来实现重建。多个实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
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.
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