Fractal compressed sensing imaging with sparse difference based on fractal and entropy recognition

摘要

Compressed sensing (CS) is a topic of great interest in many research fields, especially image processing. However, in the traditional CS framework, one disadvantage is that the computational cost of sparse representation (SR) is too high to meet basic application requirements. Another is that l(1)-norm minimisation, as the object function of CS recovery, is unsuitable for the approximation of image details. Therefore, this paper presents a novel fractal CS (FCS) framework for digital imaging. The FCS framework is basically as follows: first, the sparse difference (SD) is used to solve the hard problem of sparse representation; then, acquisition of SD is based on the results of classification under a combined fractal and entropy feature space; finally, fractal minimisation is used instead of l(1)-norm minimisation as the object function to realise high-quality CS recovery of image details. Several experiments show the feasibility and dependability of the FCS imaging framework.