论文标题
ICRICS：图像压缩感测的迭代补偿恢复
ICRICS: Iterative Compensation Recovery for Image Compressive Sensing
论文作者
论文摘要
闭环体系结构被广泛用于自动控制系统，并获得了杰出的性能。但是，经典的压缩传感系统采用了带有分离的采样和重建单元的开环体系结构。因此，通过将闭环框架引入传统压缩的传感系统中，提出了图像压缩传感（ICRIC）的迭代补偿恢复方法。所提出的方法取决于任何现有方法，并通过添加负面反馈结构来升级其重建性能。对压缩传感系统负反馈的理论分析进行了。还提供了所提出方法有效性的大致数学证明。在3个以上的图像数据集上进行的仿真实验表明，所提出的方法优于重建性能的10种竞争方法。平均峰值信号比率的最大增量为4.36 dB，一个数据集的平均结构相似性的最大增量为0.034。基于负反馈机制的提出方法可以有效地纠正现有图像压缩传感系统中的恢复误差。
Closed-loop architecture is widely utilized in automatic control systems and attain distinguished performance. However, classical compressive sensing systems employ open-loop architecture with separated sampling and reconstruction units. Therefore, a method of iterative compensation recovery for image compressive sensing (ICRICS) is proposed by introducing closed-loop framework into traditional compresses sensing systems. The proposed method depends on any existing approaches and upgrades their reconstruction performance by adding negative feedback structure. Theory analysis on negative feedback of compressive sensing systems is performed. An approximate mathematical proof of the effectiveness of the proposed method is also provided. Simulation experiments on more than 3 image datasets show that the proposed method is superior to 10 competition approaches in reconstruction performance. The maximum increment of average peak signal-to-noise ratio is 4.36 dB and the maximum increment of average structural similarity is 0.034 on one dataset. The proposed method based on negative feedback mechanism can efficiently correct the recovery error in the existing systems of image compressive sensing.
