A Reconstruction Method of Compressed Sensing 3D Medical Models Based on the Weighted 0-norm

It is an important task for physicians to reconstruct the required compressed 3D medical model, which is transferred through the Internet or stored in a 3D model library after being compressed. This paper presents a reconstruction method for compressed sensing 3D medical models. It is well known that the compressed sensing signal could be reconstructed directly via 0-norm minimizing. However, it falls into the class of NP (Non-deterministic Polynomial) problems. NP is the set of decision problems solvable in polynomial time by a theoretical non-deterministic Turing machine. In this paper, a smooth function is designed as the optimal objective function based on the signal's weighted 0-norm. Experimental results show that our method has a sound reconstruction effect and is well suitable for processing large data of 3D medical models.