In order to resolve the problem of excessive processing time and inadequate accuracy caused by existing algorithms in robot vision image reconstruction, a block variable step size adaptive compression sensor reconstruction algorithm is proposed. The algorithm integrates the regularized orthogonal matching pursuit technique in a seamlessly efficient manner to obtain consistent and accurate signal reconstruction outcomes. To apply this technique, a set of selected atoms is initialized by setting fuzzy threshold. Subsequently, inappropriate atoms are excluded, and an iterative procedure is initiated to update the set so as to approximate the signal sparsity in a stepwise fashion. In comparison with commonly used algorithms, the proposed algorithm achieved the lowest signal recovery and reconstruction error. Findings from this study indicate that our proposed hybrid paradigm may lead to positive advancement towards the development of intelligent robotic vision systems for industrial applications.

为了解决现有算法在机器人视觉图像重建中处理时间过长和精度不足的问题，提出了一种块可变步长自适应压缩传感器重建算法。该算法以无缝高效的方式集成了正则化的正交匹配追踪技术，以获得一致且准确的信号重建结果。为了应用该技术，通过设置模糊阈值来初始化一组选定的原子。随后，排除不适当的原子，并启动迭代过程以更新该集合，以便以逐步的方式近似信号稀疏度。与常用算法相比，该算法实现了最低的信号恢复和重构误差。