Huidong Dai

Adaptive compressed sampling based on extended wavelet trees.

The theory of compressed sensing (CS) indicates that a signal that is sparse or compressible can be recovered from a relatively small number of nonadaptive linear measurements that is far below the Nyquist-Shannon limit. However, CS suffers from a huge stored and computational overhead when dealing with images of high resolution, taking tens of minutes or longer. In this work, we extend the concept of wavelet trees by adding the sibling relationship and propose an imaging strategy named adaptive compressed sampling based on extended wavelet trees (EWT-ACS). Exploiting both parent-children relationship and sibling relationship in extended wavelet trees, EWT-ACS predicts the locations of significant coefficients adaptively and samples the significant coefficients using a binary digital micromirror device directly. The simulation and experimental results reveal that the proposed strategy breaks through the limitation in CS, and the reconstruction time is reduced significantly. Due to its single-pixel detection mechanism, EWT-ACS shows great potential in many imaging applications.

Colored adaptive compressed imaging with a single photodiode.

Computational ghost imaging is commonly used to reconstruct grayscale images. Currently, however, there is little research aimed at reconstructing color images. In this paper, we theoretically and experimentally demonstrate a colored adaptive compressed imaging method. Benefiting from imaging in YUV color space, the proposed method adequately exploits the sparsity of the U, V components in the wavelet domain, the interdependence between luminance and chrominance, and human visual characteristics. The simulation and experimental results show that our method greatly reduces the measurements required and offers better image quality compared to recovering the red (R), green (G), and blue (B) components separately in RGB color space. As the application of a single photodiode increases, our method shows great potential in many fields.

Adaptive compressed photon counting 3D imaging based on wavelet trees and depth map sparse representation

We demonstrate a photon counting 3D imaging system with short-pulsed structured illumination and a single-pixel photon counting detector. The proposed multiresolution photon counting 3D imaging technique acquires a high-resolution 3D image from a coarse image and details at successfully finer resolution sampled along the wavelet trees and their depth map sparse representations. Both the required measurements and the reconstruction time can be significant reduced, which makes the proposed technique suitable for scenes with high spatial resolution. The experimental results indicate that both the reflectivity and depth map of a scene at resolutions up to 512×512 pixels can be acquired and retrieved with practical times as low as 17.5 seconds. In addition, we demonstrate that this technique has ability to image in presence of partially-transmissive occluders, and to directly acquire novelty images to find changes in a scene.

Speckle-Shifting Ghost Imaging

In this paper, we introduce speckle-shifting ghost imaging (SSGI) which uses several corresponding shifted groups of speckle patterns instead of random speckle patterns in “computational ghost imaging” (CGI) to improve the performance of edge detection. The shifting of speckle patterns makes SSGI directly achieve the edges of an unknown object without the clear “ghost” images. Numerical simulations and experiments are performed. It is seen that SSGI is applicable for both binary and gray-scale objects in noisy environments. This provides a great opportunity to pave the way for the real applications of CGI in remote sensing and biological imaging.

Three-dimensional Active Imaging using Compressed Gating

Due to the numerous applications employed 3D data such as target detection and recognition, three-dimensional (3D) active imaging draws great interest recently. Employing a pulsed laser as the illumination source and an intensified sensor as the image sensor, the 3D active imaging method emits and then records laser pulses to infer the distance between the target and the sensor. One of the limitations of the 3D active imaging is that acquiring depth map with high depth resolution requires a full range sweep, as well as a large number of detections, which limits the detection speed. In this work, a compressed gating method combining the 3D active imaging and compressive sensing (CS) is proposed on the basis of the random gating method to achieve the depth map reconstruction from a significantly reduced number of detections. Employing random sequences to control the sensor gate, this method estimates the distance and reconstructs the depth map in the framework of CS. A simulation was carried out to estimate the performance of the proposed method. A scene generated by the 3ds Max was employed as target and a reconstruction algorithm was used to recover the depth map in the simulation. The simulation results have shown that the proposed method can reconstruct the depth map with slight reconstruction error using as low as 7% detections that the conventional method requires and achieve perfect reconstruction from about 10% detections under the same depth resolution. It has also indicated that the number of detections required is affected by depth resolution, noise generated by a variety of reasons and complexity of the target scene. According to the simulation results, the compressed gating method is able to be used in the case of long range with high depth resolution and robust to various types of noise. In addition, the method is able to be used for multiple-return signals measurement without increase in the number of detections.

Range walk error correction using prior modeling in photon counting 3D imaging lidar

A real-time correction method for range walk error in photon counting 3D imaging Lidar is proposed in this paper. We establish the photon detection model and pulse output delay model for GmAPD, which indicates that range walk error in photon counting 3D imaging Lidar is mainly effected by the number of photons during laser echo pulse. A measurable variable – laser pulse response rate is defined as a substitute of the number of photons during laser echo pulse, and the expression of the range walk error with respect to the laser pulse response rate is obtained using priori calibration. By recording photon arrival time distribution, the measurement error of unknown targets is predicted using established range walk error function and the range walk error compensated image is got. Thus real-time correction of the measurement error in photon counting 3D imaging Lidar is implemented. The experimental results show that the range walks error caused by the difference in reflected energy of the target can be effectively avoided without increasing the complexity of photon counting 3D imaging Lidar system.

Adaptive compressed photon counting 3D imaging based on wavelet trees and Hadamard multiplexing

A photon counting 3D imaging system with short-pulsed structured illumination and a single-pixel photon counting detector is built. The proposed multiresolution photon counting 3D imaging technique acquires a high-resolution 3D image from a coarse image and details at successfully finer resolution sampled by Hadamard multiplexing along with the wavelet trees. The detected power is significant increased thanks to the Hadamard multiplexing. Both the required measurements and the reconstruction time can be significant reduced, which makes the proposed technique suitable for scenes with high spatial resolution. Since the depth map is retrieved through a linear inverse Hadamard transform instead of the computational intensive optimization problems performed in CS, the time consumed to retrieve the depth map can be also reduced, and thus it will be suitable for applications of real-time compressed 3D imaging such as object tracking. Even though the resolution of the final 3D image can be high, the number of measurements remains small due to the adaptivity of the wavelet-trees-based sampling strategy. The adaptive sampling technique is quality oriented, allowing more control over the image quality. The experimental results indicate that both the intensity image and depth map of a scene at resolutions up to 512×512 pixels can be acquired and retrieved with practical times as low as 17 seconds.

Colored adaptive compressed imaging using color space conversion

Computational ghost imaging (CGI) is mainly used to reconstruct grayscale images at present and there are few researches aiming at color images. In this paper, we both theoretically and experimentally demonstrate a colored adaptive compressed imaging method. Benefiting from imaging in YUV color space, the proposed method adequately exploits the sparsity of U, V components in the wavelet domain, the interdependence between luminance and chrominance, and the human visual characteristics. The simulation and experimental results show that our method greatly reduces the measurements required, and offers better image quality compared to recovering red (R), green (G) and blue (B) components separately in RGB color space. As the application of single photodiode increases, our method shows great potential in many fields.

Adaptive Target Profile Acquiring Method for Photon Counting 3-D Imaging Lidar

A direct-detection 3-D imaging lidar is capable of acquiring a depth image of noncooperative targets in long distance, using Geiger mode avalanche photodiodes and the technique of time-correlated single-photon counting. However, conventional 3-D imaging lidar has a long data acquisition time. This paper introduces a spatially adaptive method to obtain target profile rapidly for 3-D imaging lidar by exploiting discontinuities between targets and background in the depth domain. The idea behind our strategy is using an adaptive scanning step to localize the regions near the depth boundaries and perform fine scans only to these regions. Fine scans only for those specific regions ensure the recovery accuracy while consuming much less data acquisition time, compared to a conventional high-resolution scanning lidar. Experimental results demonstrate that for the experimental scene, the data acquisition time decreases 85% by using the proposed method, without generating much distortion of the target profile. This method may be of considerable value to fields that need fast 3-D imaging, such as remote sensing and military reconnaissance.

Colored adaptive compressed imaging in YUV color space

Benefiting from imaging in YUV color space, the proposed method adequately exploits the sparsity of U, V components in the wavelet domain, the interdependence between luminance and chrominance, and the human visual characteristics.