Computational ghost imaging based on a conditional generation countermeasure network under a low sampling rate.

In this paper, an end-to-end depth neural network based on a conditional generative adversarial network for computational ghost imaging (CGANCGI) is proposed to restore clear object images with high quality at a sub-Nyquist sampling rate. The 2D light signal collected by a CMOS camera and the gray image of the original measured object are used as the input of the network model; then, the CGANCGI network is trained, and the measured object image is recovered directly from the 2D light signal. Experiments have verified that the proposed method only needs 1/10 of traditional deep learning samples to achieve fast image restoration with high-quality, and its peak signal-to-noise ratio and structural similarity are, respectively, four to six times and five to seven times higher than those of the original image, which prove that our method has practical application prospects in ghost imaging under low sampling rates.

High-speed computational ghost imaging based on an auto-encoder network under low sampling rate.

Computational ghost imaging is difficult to apply under low sampling rate. We propose high-speed computational ghost imaging based on an auto-encoder network to reconstruct images with high quality under low sampling rate. The auto-encoder convolutional neural network is designed, and the object images can be reconstructed accurately without labeled images. Experimental results show that our method can greatly improve the peak signal-to-noise ratio and structural similarity of the test samples, which are up to 18 and 0.7, respectively, under low sampling rate. Our method only needs 1/10 of traditional deep learning samples to achieve fast and high-quality image reconstruction, and the network also has a certain generalization to the gray-scale images.

Specular highlight removal based on an iterative light field method.

In this paper, a novel, to the best of our knowledge, iterative approach for highlight removal is proposed using lenselet-based plenoptic cameras without multiple exposures. An unsupervised k-means clustering approach that relates unsaturated pixels to chromatic dispersion based on the intrinsic decomposition and dichromatic reflection model is proposed to recover unsaturated highlights. Meanwhile, an adaptive direction method along with a Gaussian probability distribution model is designed to recover the saturated highlights. Finally, a method that combines the specular residual ratio with information entropy is built to quantitatively evaluate the quality of highlight removal. Generally, our method not only fully removes specular highlights, but also has low spatial complexity of image acquisition, more stability, and outstanding restoration for complex scenes.

3D reconstruction of structured light fields based on point cloud adaptive repair for highly reflective surfaces.

In this paper, a novel method, to the best of our knowledge, of structured light fields based on point cloud adaptive repair is proposed to realize 3D reconstruction for highly reflective surfaces. We have designed and built a focused light field camera whose spatial and angular resolution can be flexibly adjusted as required. Then the subaperture image extraction algorithm based on image mosaic is deduced and presented to obtain multidirectional images. After that, the 3D reconstruction of structured light field imaging based on point cloud adaptive repair is presented to accurately reconstruct for highly reflective surfaces. In addition, a method based on smoothness and repair rate is also proposed to objectively evaluate the performance of the 3D reconstruction. Experimental results demonstrate the validity of the proposed method to perform high-quality depth reconstruction for highly reflective surfaces. Generally, our method takes advantage of the multidirectional imaging of the light field camera and can ensure good modulation effect of structured light while avoiding hardware complexity, which makes it application more convenient.

Three-Dimensional Reconstruction of Light Field Based on Phase Similarity.

Light field imaging plays an increasingly important role in the field of three-dimensional (3D) reconstruction because of its ability to quickly obtain four-dimensional information (angle and space) of the scene. In this paper, a 3D reconstruction method of light field based on phase similarity is proposed to increase the accuracy of depth estimation and the scope of applicability of epipolar plane image (EPI). The calibration method of the light field camera was used to obtain the relationship between disparity and depth, and the projector calibration was removed to make the experimental procedure more flexible. Then, the disparity estimation algorithm based on phase similarity was designed to effectively improve the reliability and accuracy of disparity calculation, in which the phase information was used instead of the structure tensor, and the morphological processing method was used to denoise and optimize the disparity map. Finally, 3D reconstruction of the light field was realized by combining disparity information with the calibrated relationship. The experimental results showed that the reconstruction standard deviation of the two objects was 0.3179 mm and 0.3865 mm compared with the ground truth of the measured objects, respectively. Compared with the traditional EPI method, our method can not only make EPI perform well in a single scene or blurred texture situations but also maintain good reconstruction accuracy.

[Meta-analysis of the association between angiotension-converting enzyme I/D polymorphism and susceptibility to children with Henoch-Schönlein purpura or Henoch-Schölein purpura nephritis].

OBJECTIVE: To explore the relationship between angiotension-converting enzyme (ACE) I/D polymorphism and susceptibility to Henoch-Schönlein purpura or Henoch-Schönlein purpura nephritis (HSP/HSPN) especially among Chinese children. METHODS: The publications up to December 31, 2013 from PubMed, Embase, Google Scholar, Springer Link, Highwire, Cochrane, ISI, CNKI and Wanfang databases were searched for relevant literature on the association between ACE I/D polymorphism and pediatric HSP/HSPN. Heterogeneity was evaluated. And Stata 12.0 was used to assess the association strength in terms of odds ratio (OR) and 95% CI. RESULTS: A total of 11 case-control studies fulfilled the inclusion criteria. And 5 articles were indentified for analyzing the relationship between ACE I/D polymorphism and pediatric HSP risk, involving 512 cases and 631 controls.Significantly risks of HSP were found (D/I: OR (95% CI) = 1.23 (1.04-1.46), P < 0.05; DD/DI + II:OR (95% CI) = 1.24 (0.77-2.00), P > 0.05 and II/DD+DI: 0.68 (0.52-0.90), P < 0.05). Nine articles were indentified for analyzing the relationship between ACE I/D polymorphism and pediatric HSPN risk, involving 446 cases and 877 controls.Significant risks of pediatric HSPN were found (D/I: OR (95% CI) = 1.52 (1.14-2.04), P < 0.05; DD/DI+II: OR(95% CI) = 1.85 (1.06-3.21), P < 0.05; II/DD+DI: OR(95%CI) = 0.62 (0.47-0.82), P < 0.05). CONCLUSIONS: In different ethnic groups, genotype DD of ACE I/D may increase the risk of pediatric HSP/HSPN and thus constitute a risk factor. Genotype II of ACE I/D may decrease the risks of pediatric HSP/HSPN and it may be a constitutively protective factor for pediatric HSP/HSPN.