Compressive ghost imaging in scattering media guided by region of interest

Abstract

Compressive ghost imaging (CSGI) combines structured illumination and a bucket detector for obtaining the light intensity signal from an unknown object. Light fluctuations are generated by few measurements and then an image is reconstructed using optimization algorithms such as compressive sensing (CS) by finding its sparse representation. The measured light fluctuations are not sensitive to scattering degradation. Consequently, an absorbing object completely embedded in a scattering media can be imaged. To speed up the sequential loading of illumination patterns on a digital micromirror device and achieve data compression, the sampling number should be reduced because more than 90% of the time in CS reconstruction is usually spent in getting a frame of image. In this study, we propose a novel strategy to realize a speedy and reliable reconstruction procedure for obtaining a high image quality by using prior knowledge during the acquisition of light intensity signal in CSGI. The prior knowledge is established by extracting the features of a local target area, which is designed by descattering of images with extra noise using fast Fourier single-pixel imaging. The proposed method facilitates a reliable image quality even under the reduction in the compression ratio, thereby overcoming the limitation of the dependence of sampling ratio on the image quality in CSGI.