Abstract：The standard Compressed Sensing (CS) reconstructions of image exploit simply the sparse priors of the wavelet coefficients, ignoring the structural information of the wavelet coefficients. In this paper, the Hidden Markov Tree ( HMT ) model is integrated in the compressed sensing，which has been found successful in capturing the key features of the joint probability density of the wavelet coefficients of real-world image. An optimization issue which is similar to the standard compressed sensing is derived from the MAP reconstructions for the image based on HMT model, and an alternating convex projection algorithm based on Bayesian optimization is proposed. What’s more, a universal HMT (uHMT) model based on the dual-tree wavelet transform and its improved form are integrated to improve the reconstruction performance further, instead of the HMT model of the orthogonal wavelet transform. As the experiments show, the average Peak Signal-to-Noise Ratio (PSNR) of the reconstructed image based on the improved uHMT (iuHMT) model in the dual-tree wavelets domain outperforms uHMT model 0.97 dB.
练秋生, 王艳. 基于双树小波通用隐马尔可夫树模型的图像压缩感知[J]. 电子与信息学报, 2010, 32(10): 2301-2306.
Lian Qiu-Sheng, Wang Yan. Image Compressed Sensing Based on Universal HMT of the Dual-tree Wavelets. , 2010, 32(10): 2301-2306.
2010, Vol. 32 
