The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video
This work addresses the problem of real-time signal processing for embedded devices, offering a hybrid approach that combines conventional and compressive sensing.
The paper tackles the challenge of reconstructing high-resolution signals from under-sampled data in compressed sensing by introducing the STONE transform, which enables instant reconstruction at Nyquist rates and enhancement to higher resolutions, demonstrated through a real-time compressive video camera.
Compressed sensing enables the reconstruction of high-resolution signals from under-sampled data. While compressive methods simplify data acquisition, they require the solution of difficult recovery problems to make use of the resulting measurements. This article presents a new sensing framework that combines the advantages of both conventional and compressive sensing. Using the proposed \stone transform, measurements can be reconstructed instantly at Nyquist rates at any power-of-two resolution. The same data can then be "enhanced" to higher resolutions using compressive methods that leverage sparsity to "beat" the Nyquist limit. The availability of a fast direct reconstruction enables compressive measurements to be processed on small embedded devices. We demonstrate this by constructing a real-time compressive video camera.