Colour compressed sensing imaging via sparse difference and fractal minimisation recovery

In colour compressed sensing (CS) imaging, the current two bottlenecks for application are (1) high computation cost of sparse representation (SR) with over-complete dictionary and (2) unsatisfactory imaging quality of CS recovery with l₁-norm minimisation. Thus, this study proposes a novel colour CS imaging framework. In the framework, two improvements are achieved: (1) the authors present the sparse difference to reduce the computation cost of SR in RGB colour imaging; (2) the authors use fractal dimension instead of l₁-norm as the object function to actualise high quality CS recovery. The feasibility of our colour CS imaging framework is proved by sseveral experiments.