Fast compressed sensing analysis for super-resolution imaging using L1-homotopy.

摘要: In super-resolution imaging techniques based on single-molecule switching and localization, the time to acquire a image is limited by maximum density of fluorescent emitters that can be accurately localized per frame. order increase rate, several methods have been recently developed analyze images with higher emitter densities. One powerful approach uses compressed sensing analyzable frame several-fold compared other reported approaches. However, computational cost this approach, which interior point methods, high, analysis typical 40 µm x field-of-view movie requires thousands hours high-end desktop personal computer. Here, we demonstrate an alternative compressed-sensing algorithm, L1-Homotopy (L1H), generate reconstructions are essentially identical those derived using in one two orders magnitude less depending density. Moreover, for experimental data set varying density, L1H ~300-fold faster than methods. This drastic reduction should allow routinely applied analysis.