For conventional imaging, the imaging resolution limit is given by the Rayleigh criterion. Exploiting the prior knowledge of imaging objects sparsity and fixed optical system, imaging beyond the conventional Rayleigh limit, which is backed up by nume
rical simulation and experiments, is achieved by illuminating the object with single-shot thermal light and detecting the objects information at the imaging plane with some sparse-array single-pixel detectors. The quality of sub-Rayleigh imaging with sparse detection is also shown to be related to the effective number of single-pixel detectors and the detection signal-to-noise ratio at the imaging plane.
Based on compressive sampling techniques and short exposure imaging, super-resolution imaging with thermal light is experimentally demonstrated exploiting the sparse prior property of images for standard conventional imaging system. Differences betwe
en super-resolution imaging demonstrated in this letter and super-resolution ghost imaging via compressive sampling (arXiv. Quant-ph/0911.4750v1 (2009)), and methods to further improve the imaging quality are also discussed.
Both ghost imaging (GI) and ghost imaging via compressive sampling (GICS) can nonlocally image an object. We report the influence of spatial transverse coherence property of a thermal source on GI and GICS and show that, using the same acquisition nu
mbers, the signal-to-noise ratio (SNR) of images recovered by GI will be reduced while the quality of reconstructed images will be enhanced for GICS as the spatial transverse coherence lengths located on the object plane are decreased. Differences between GI and GICS, methods to further improve the quality and image extraction efficiency of GICS, and its potential applications are also discussed.
Much more image details can be resolved by improving the systems imaging resolution and enhancing the resolution beyond the systems Rayleigh diffraction limit is generally called super-resolution. By combining the sparse prior property of images with
the ghost imaging method, we demonstrated experimentally that super-resolution imaging can be nonlocally achieved in the far field even without looking at the object. Physical explanation of super-resolution ghost imaging via compressive sampling and its potential applications are also discussed.
For ghost imaging, pursuing high resolution images and short acquisition times required for reconstructing images are always two main goals. We report an image reconstruction algorithm called compressive sampling (CS) reconstruction to recover ghost
images. By CS reconstruction, ghost imaging with both super-resolution and a good signal-to-noise ratio can be obtained via short acquisition times. Both effect influencing and approaches further improving the resolution of ghost images via CS reconstruction, relationship between ghost imaging and CS theory are also discussed.
