基于小波树结构和迭代收缩的图像压缩感知算法研究
doi: 10.3724/SP.J.1146.2010.00684
Image Compressed Sensing Algorithm Based on Wavelet Tree Structure and Iterative Shrinkage
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摘要: 模型化压缩感知图像重构在标准压缩感知重构的基础上利用了小波树结构的先验知识，分别用贪婪树逼近和最优树逼近的方法求解重构优化问题。该文以模型化压缩感知重构中已有的小波树结构为基础，依据对大量自然图像小波系数关系的统计结果，提出了基于相邻系数、父系数与子系数之间统计相依关系的小波系数合理树结构，并结合小波系数合理树结构的思想，改进了普通迭代硬阈值压缩感知图像重构算法和基于最优树的模型化压缩感知图像重构算法。实验结果表明，该文算法能获得更高的图像重构质量。Abstract: Based on the standard compressed sensing, the model-based Compressed Sensing (CS) uses the tree structure priors, and solves the optimal reconstruction problem with two existing tree structure approximation which are greedy tree approximation and optimal tree approximation. Through numerous statistics test of wavelet relationship, a new tree structure which is named reasonable tree structure is proposed, which is based on the relationship between neighbor coefficients, parent coefficients and children coefficients. What is more, combining with the new reasonable tree structure, an improvement is made for the iterative hard threshold reconstruction algorithm and model-based compressed sensing reconstruction algorithm. Comparing with the iterative hard threshold algorithm and model-based compressed sensing algorithm, the proposed algorithm can achieve higher image reconstruction performance.
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