基于压缩感知的阈值多路径稀疏度自适应图像重构算法

针对深度优先的多路径匹配追踪算法在进行图像重构时需要已知图像稀疏度、计算复杂度高等问题,提出了阈值多路径稀疏度自适应图像重构算法。该算法引入多个候选集,通过设定阈值来进行原子筛选和候选集数量的调整。然后每次迭代选出残差最小的路径作为新的候选集,以提高重构速度。此外,将残差差分小于某一阈值作为算法停止条件,因此不需要图像稀疏度作为算法的输入。实验结果表明,该算法可以获得较好的重构效果,同时保持了良好的时间复杂度和抗噪性能。

Threshold multipath sparsity adaptive image reconstruction algorithm based on compressed sensing

Aiming at the problem that depth-first multipath matching pursuit algorithm needs known image sparsity and high computational complexity in image reconstruction, a threshold multipath sparsity adaptive image reconstruction algorithm is proposed. In this algorithm, multiple candidate sets are introduced, and thresholds are set to select atoms and adjust the number of candidate sets. Then each iteration selects the path with the smallest residual as a new candidate set to improve the reconstruction speed. In addition, residual difference less than a threshold is used as the stopping condition of the algorithm, so image sparsity is not needed as the input of the algorithm. The experimental results show that the algorithm can achieve good reconstruction effect, while maintaining good time complexity and anti-noise performance.