Weighted singular value thresholding for sparse photoacoustic microscopy

In the sparse Photoacoustic Microscopy system, a uniform random sampling scheme and low-rank matrix approximation-GoDec algorithm have been proposed for fast data acquisition and image recovery. However, this low-rank approximation algorithm leads to low resolution and fuzzy structure details. In this paper, the weighted Singular Value Thresholding algorithm is first applied in sparse PAM system to recover PAM images with high resolution. An efficient iterative weighting scheme is used for exactly solving matrix completion problem. Therefore, this algorithm is more accurate than classic SVT algorithm, which is proved by singular value analysis. Moreover, both simulations and real data experiments verify the performance of the weighted SVT algorithm and the application potential of this modified sparse PAM system.