Enrong Li

Ghost imaging lidar via sparsity constraints

For remote sensing, high-resolution imaging techniques are helpful to catch more characteristic information of the target. We extend pseudo-thermal light ghost imaging to the area of remote imaging and propose a ghost imaging lidar system. The experimental results demonstrate that the real-space image of a target at about 1.0 km range with 20 mm resolution is achieved by ghost imaging via sparsity constraints (GISC) technique. The characters of GISC technique compared to the existing lidar systems are also discussed.

Structured image reconstruction for three-dimensional ghost imaging lidar

A structured image reconstruction method has been proposed to obtain high quality images in three-dimensional ghost imaging lidar. By considering the spatial structure relationship between recovered images of scene slices at different longitudinal distances, orthogonality constraint has been incorporated to reconstruct the three-dimensional scenes in remote sensing. Numerical simulations have been performed to demonstrate that scene slices with various sparse ratios can be recovered more accurately by applying orthogonality constraint, and the enhancement is significant especially for ghost imaging with less measurements. A simulated three-dimensional city scene has been successfully reconstructed by using structured image reconstruction in three-dimensional ghost imaging lidar.

Ghost imaging of a moving target with an unknown constant speed

We report a method for ghost imaging of a moving target with an unknown constant speed. By both numerical simulations and experiments, it is shown that by matching the calculated signal to the recorded data, the unknown speed can be correctly retrieved and the target image can be reconstructed. We suppose that this work is a good start of addressing the related problems and will find its applications in ghost imaging remote sensing.

Spectral Camera based on Ghost Imaging via Sparsity Constraints

The spectral camera based on ghost imaging via sparsity constraints (GISC spectral camera) is a phase modulated compressive snapshot spectral imager. It makes use of the second-order intensity correlation of the light field to resolve the spatial and spectral information. In this paper, an optical design for GISC spectral camera which aims to obtain the desired spatial and spectral resolution is presented. A system calibration strategy based on few-mode optical fiber and monochrometer is developed. The snapshot spectral imaging experiments for the test targets and natural scenes are conducted using the prototype of GISC spectral camera loaded on the tethered balloon. The result of the spatial resolution, linearity, and spectra reconstruction error of the prototype is quantitatively evaluated. The distinguishable size at the distance of 1 km is around 0.34 m. The linearity is higher than 0.99 among the wavelength channels from 410 to 640 nm. The reconstructed spectra of eight color targets are compared with those measured by a commercial spectroradiometer. The average relative root mean squared error of the reconstructed spectra is 0.65.

Morphology separation in ghost imaging via sparsity constraint

The separation of morphology components in ghost imaging via sparsity constraint is investigated by adapting the morphology component analysis technique based on the fact that different morphology components can be sparsely expressed in different basis. The successful separation of reconstructed image plays an important role in the ability to identify it, analyze it, enhance it and more. This approach is first studied with numerical simulations and then verified with both table-top and outdoor experimental data. Results show that it can not only separate different morphology components but also improve the quality of the reconstructed image.

Experimental investigation of the quality of ghost imaging via sparsity constraints

Sampling number and detection signal-to-noise ratio (SNR) are two major factors influencing imaging quality. Combining the images sparsity in the representation basis with the ghost imaging (GI) approach, GI via sparsity constraints (GISC) can nonlocally image the object even when the measurement number is far fewer than the Nyquist criteria required for the conventional GI reconstruction algorithm. The influence of receiving the systems numerical aperture and detection SNR in the test path to GISC is studied through experiments. It is also shown that the quality of GISC depends on the objects sparse representation basis.

Application of multi-correlation-scale measurement matrices in ghost imaging via sparsity constraints.

Sampling and reconstruction techniques are of special interest and importance in ghost imaging. Up to now, the transverse correlation scale of measurement matrices are usually constant. This paper explores a new possibility of constructing highly efficient measurement matrices with multi-correlation scales. Comparisons between the simulational and experimental results show that the multi-correlation-scale measurement matrices are highly efficient and accurate in sampling and image reconstruction and have a better antinoise ability than the existing constant-correlation-scale measurement matrices.

Multiple-input ghost imaging via sparsity constraints with thermal light

Single-pixel detection is one of the advantages of ghost imaging via sparsity constraints (GISC), but numerous modulations from the source are usually required to obtain an image with a good signal-to-noise ratio. When the spatial information at the detection plane is taken into consideration and recorded by sparse-array single-pixel detectors, we experimentally demonstrate that multiple-input ghost imaging via sparsity constraints (MI-GISC) with thermal light can further reduce the source’s modulations compared with GISC. Factors affecting the imaging quality of MI-GISC with thermal light are also discussed.

Computational spectral imaging based on random modulation and compressed sensing reconstruction algorithm

Spectral imaging based on speckle modulation and compressed sensing algorithm is proposed.The work principle for the imaging system and the reconstruction algorithm is revealed.The comparative experiment with the traditional spectral imaging is conducted which suggested that the computational spectral imaging strategy is feasible.