Multivariate Compressive Sensing for Image Reconstruction in the Wavelet Domain: Using Scale Mixture Models
Multivariate Compressive Sensing for Image Reconstruction in the Wavelet Domain: Using Scale Mixture Models
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DOI：
10.1109/tip.2011.2150231
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发表时间：
2011-12
影响因子：
10.6
通讯作者：
Jiao Wu;Fang Liu;L. Jiao;Xiaodong Wang;B. Hou
中科院分区：
文献类型：
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作者：
Jiao Wu;Fang Liu;L. Jiao;Xiaodong Wang;B. Hou
大多数基于小波的压缩感知（CS）重建方法都是在小波系数独立假设下开发的。然而，图像的小波系数具有显着的统计依赖性。人们已经提出了许多图像小波系数的多元先验模型，并成功应用于图像估计问题。本文考虑小波系数的统计结构来对小波域中的稀疏或压缩图像进行CS重建。开发了一种基于多元模型的多元追踪算法（MPA）。几个多元尺度混合模型被用作 MPA 的先验分布。我们的方法通过对邻域中小波系数的统计依赖性进行建模来重建图像。与许多最先进的压缩感知重建算法相比，基于这些尺度混合模型所提出的算法提供了优越的性能。
Most wavelet-based reconstruction methods of compressive sensing (CS) are developed under the independence assumption of the wavelet coefficients. However, the wavelet coefficients of images have significant statistical dependencies. Lots of multivariate prior models for the wavelet coefficients of images have been proposed and successfully applied to the image estimation problems. In this paper, the statistical structures of the wavelet coefficients are considered for CS reconstruction of images that are sparse or compressive in wavelet domain. A multivariate pursuit algorithm (MPA) based on the multivariate models is developed. Several multivariate scale mixture models are used as the prior distributions of MPA. Our method reconstructs the images by means of modeling the statistical dependencies of the wavelet coefficients in a neighborhood. The proposed algorithm based on these scale mixture models provides superior performance compared with many state-of-the-art compressive sensing reconstruction algorithms.