Compressive Sensing Reconstruction Algorithm Based on Heavy-Ball Momentum

Nowadays, the digital world shows the development trend of high-dimensional mass, and most data can be structured or sparsely represented, so it is convenient to use Compressive Sensing (CS) to reduce the storage and transmission costs of big data. The key data reconstruction process in CS is usually formalized as a minimization problem, but it is difficult for existing algorithms to achieve high data reconstruction speed and accuracy at the same time. Heavy-ball momentum algorithm is the most representative acceleration algorithm in the field of optimization. In this paper, it is introduced into the reconstruction process of CS, and the convergence of the momentum algorithm to decompress the CS reconstruction optimization problem is guaranteed through theoretical analysis. The results of comparative experiments also show that the momentum algorithm can reconstruct the image with higher quality and consume the least computing time.