贝叶斯小波图像压缩感知方法

Wavelet Image Compressed Sensing Based on Bayesian Model

现有小波图像压缩感知方法大多利用父子小波系数的相关性来提高重构精度,很少考虑同一尺度兄弟小波系数间的相关性关系.鉴于此,提出一种基于贝叶斯模型的高频系数联合重构小波图像压缩感知方法.该算法将同一尺度水平、垂直和对角三个方向高频系数分别压缩感知采样,然后设计分层贝叶斯模型,充分利用此三个方向兄弟小波系数的相关性来重构图像.实验结果表明本文提出的方法比传统的多尺度压缩感知有更高的图像重构质量.

Most image compressed sensing algorithms improve the reconstruction quality by utilizing the correlation of parent-child wavelet coefficients. However, few people study the compressed sensing based on the fraternal relationship of the high-frequency coefficients. In this paper, a Bayesian-based image compressed sensing algorithm using joint reconstruction of high-frequency wavelet coefficients is proposed. Firstly, the high-frequency coefficients of the horizontal, vertical and diagonal directions in the same scale are sampled separately when executing compressed sensing. Then, a hierarchical Bayesian model is presented and the correlation is used when reconstruction is performed. Experimental results show that our proposed algorithm has higher image reconstruction quality than the existed MCS.