Abstract: The unsuitable iterative number of the Regularized Orthogonal Matching Pursuit (ROMP) algorithm in the framework of Compressive Sensing (CS) may reduce the quality of image reconstruction greatly. In this paper, an algorithm was proposed to determine the proper iterative number. In order to guarantee the quality of image reconstruction, the best iterative number of every image block with various sparsity was obtained by using the prior knowledge which resulted from last iteration. The experimental results show that the method can get better reconstruction performance than the ROMP algorithm with deterministic iterative number.

Key words: Compressive Sensing (CS), image construction, Regularized Orthogonal Matching Pursuit (ROMP), sparse representation

摘要： 在压缩感知框架下运用正则化正交匹配追踪(ROMP)算法进行图像重构时,迭代次数取值不合适会严重降低重构图像的质量。针对这一问题,提出了确定合理迭代次数的方法。将以往迭代得出的结果作为先验知识,获取具有不同稀疏程度图像块的最佳迭代次数,从而保证了整幅图像的重构质量。实验表明,该方法重构效果优于采用固定迭代次数的ROMP算法。

关键词: 压缩感知, 图像重构, 正则化正交匹配追踪, 稀疏表示