Compressive sensing ghost imaging object detection using generative adversarial networks

Zhai, Xiang; Cheng, Zhengdong; Yuan, Wei; Liang, Zhenyu; Chen, Yi

doi:10.1117/1.oe.58.1.013108

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…Currently, several task-oriented GI studies have been performed, mainly realized by designing the encoding light-field patterns according to the desired task information and assisting with data-processing methods. They can be classified into several categories, including nonimaging object detection [108], non-imaging object classification [109,110], non-imaging object edge detection [111,112], and object tracking [113] as well as progressive imaging [114]. In brief, these studies largely utilize the flexibility of the information mapping mode of GI systems.…”

Section: Task-oriented Gi System Designmentioning

confidence: 99%

On Ghost Imaging Studies for Information Optical Imaging

Han

2022

Applied Sciences

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Since the birth of information theory, to understand, study, and optimize optical imaging systems from the information–theoretic viewpoint has been an important research subfield of optical imaging, accompanied by a series of corresponding advances. However, since the “direct point-to-point” image information acquisition mode of traditional optical imaging systems, which directly performs one-to-one signal mapping from the object to the detection plane, lacks a “coding–decoding” operation on the image information, related studies based on information theory are more meaningful in the theoretical sense, while almost acting as icing on the cake for the optimization and design of practical systems and contributing little to substantive breakthroughs in further imaging capabilities. With breakthroughs in modern light-field modulation techniques as well as ghost imaging techniques, which establish point-to-point image signal reproduction based on high-order correlation of light fields, currently, it is able to encode the image information with controllable spatiotemporal light-field fluctuations during the ghost imaging process. Combined with modern digital photoelectric detection technologies, ghost imaging systems behave more in line with the modulation–demodulation information transmission mode compared to traditional optical imaging. This puts forward imperative demands and challenges for understanding and optimizing ghost imaging systems from the viewpoint of information theory, as well as bringing more development opportunities for the research field of information optical imaging. This article will briefly review the development of information optical imaging since the birth of information theory, overview its current research status by combining with latest related progresses in ghost imaging, and discuss the potential developing tendency of this research topic.

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Section: Task-oriented Gi System Designmentioning

confidence: 99%

On Ghost Imaging Studies for Information Optical Imaging

Han

2022

Applied Sciences

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“…By observing the structure of the generative confrontation network in Figure 1, GAN is mainly composed of two parts: generation network (G) and discriminant network (D). And, it uses a competing mechanism [11][12][13]; the purpose is to enable the generator to generate data similar to the real data distribution, which made the GAN achieve the effect of being fake. Aiming at the problem of data prediction, the generator is trained to learn the data distribution and predict the image distribution data.…”

Section: Generative Adversarial Network (Gan)mentioning

confidence: 99%

Adaptive Adjustment Object Detection Algorithm under Multiple Mechanisms Based on GAN

Qiu

Wang

Pan

2021

Scientific Programming

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Target tracking is prone to problems such as target loss and identity jump when the target is occluded and the attitude is changed. In order to solve this phenomenon, this paper proposes the adaptive adjustment object detection algorithm under multiple mechanisms based on GAN. This algorithm introduces a gradient penalty mechanism to the discriminator and uses the relative discriminator structure to reconstruct the discriminator, so as to improve the discriminatory ability of the discriminator. Then, through the feedback mechanism, the obtained data is fed back to the generator network in time, and the genetic mechanism is used to speed up the positioning of the key areas of the image. Experimental results show that compared with other existing algorithms, this algorithm can effectively locate and distinguish under different environments. And, the target still maintains a high resolution. When the target is occluded, it can effectively avoid the phenomenon of target loss and identity jump.

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“…As per the measurement Nyquist limit, if the number of resolution units of the covered object is N, then we need N different intensity patterns, in which N = M, to reconstruct the object. The recent works [10][11][12][13][14] showed that utilizing the compressive sensing (CS) reconstruction algorithm can reduce the number of measurements, i.e. M < N. Specifically, the goal of the CS reconstruction algorithm is to reconstruct image O(x, y) by minimizing the L 1 -norm on the sparse basis, i.e.…”

Section: Imaging Schemementioning

confidence: 99%

“…ghost imaging (CSGI) [8][9][10][11][12][13][14], and computational ghost imaging (CGI) schemes [15][16][17][18][19][20], many GI applications have been discovered. Although GI has significant advantages over traditional imaging methods, its imaging speed and quality are still big barriers.…”

Section: Introductionmentioning

confidence: 99%

Single-arm ghost imaging via conditional generative adversarial network

Zhao

Zhang

2021

Laser Phys. Lett.

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In this study, we develop a single-arm ghost imaging (GI) framework based on a conditional generative adversarial network (cGAN) to improve the image quality and extend the application scenarios of GI. A set of one-dimensional (1D) bucket signals generated by a single-arm GI system and their corresponding ground-truth counterparts is employed to train the cGAN. This allows us to reconstruct a low-noise image from a new 1D bucket signal, while the sequence of random patterns is unnecessary. The results show that the proposed method significantly improves the image quality relative to the conventional GI methods.

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Compressive sensing ghost imaging object detection using generative adversarial networks

Cited by 11 publications

References 18 publications

On Ghost Imaging Studies for Information Optical Imaging

On Ghost Imaging Studies for Information Optical Imaging

Adaptive Adjustment Object Detection Algorithm under Multiple Mechanisms Based on GAN

Single-arm ghost imaging via conditional generative adversarial network

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