Designing for Compressive Sensing: Compressive Art, Camouflage, Fonts, and Quick Response Codes

Compressive sensing (CS) is a relatively new field that has caused a lot of excitement in the signal processing community. It has superseded Shannon's time-honored sampling theorem, which states that the sampling rate of a signal must be at least twice its highest frequency. In CS, the necessary sampling rate depends on the sparsity of signal, not its highest frequency, reducing sampling requirements for many signals that exhibit natural sparsity. This compression happens on the hardware level, allowing systems to be designed with benefits ranging from increased resolution and frame rates to decreased power consumption and memory usage. Despite this enthusiasm for CS and the large quantity of research being performed, the number of commercial systems that use CS is relatively few. The problem of designing a CS strategy that increases functionality while actually reducing overall system cost has not been solved in many areas. This is a developing field where not only are new applications for CS still being developed, but also fundamental aspects of CS theory are still evolving.