scholarly journals Gradient-Descent-like Ghost Imaging

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7559
Author(s):  
Wen-Kai Yu ◽  
Chen-Xi Zhu ◽  
Ya-Xin Li ◽  
Shuo-Fei Wang ◽  
Chong Cao
Keyword(s):  
Artificial Intelligence ◽  
Image Quality ◽  
Gradient Descent ◽  
Imaging Technique ◽  
Optimal Solution ◽  
Imaging Method ◽  
Ghost Imaging ◽  
High Quality ◽  
Intensity Correlation ◽  
Optical Imaging Technique

Ghost imaging is an indirect optical imaging technique, which retrieves object information by calculating the intensity correlation between reference and bucket signals. However, in existing correlation functions, a high number of measurements is required to acquire a satisfied performance, and the increase in measurement number only leads to limited improvement in image quality. Here, inspired by the gradient descent idea that is widely used in artificial intelligence, we propose a gradient-descent-like ghost imaging method to recursively move towards the optimal solution of the preset objective function, which can efficiently reconstruct high-quality images. The feasibility of this technique has been demonstrated in both numerical simulation and optical experiments, where the image quality is greatly improved within finite steps. Since the correlation function in the iterative formula is replaceable, this technique offers more possibilities for image reconstruction of ghost imaging.

scholarly journals Magneto-optical imaging technique for hostile environments: The ghost imaging approach

2015 ◽  
Vol 106 (26) ◽  
pp. 262405 ◽  
Author(s):  
A. Meda ◽  
A. Caprile ◽  
A. Avella ◽  
I. Ruo Berchera ◽  
I. P. Degiovanni ◽  
...  
Keyword(s):  
Optical Imaging ◽  
Imaging Technique ◽  
Ghost Imaging ◽  
Imaging Approach ◽  
Optical Imaging Technique ◽  
Hostile Environments

scholarly journals Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN

2021 ◽  
Vol 13 (19) ◽  
pp. 3800
Author(s):  
Lei Fan ◽  
Yang Zeng ◽  
Qi Yang ◽  
Hongqiang Wang ◽  
Bin Deng
Keyword(s):  
Image Quality ◽  
Three Dimensional ◽  
Super Resolution ◽  
Radar Imaging ◽  
Imaging Method ◽  
Spectrum Estimation ◽  
High Quality ◽  
Imaging Geometry ◽  
Imaging Time ◽  
Model Training

High-quality three-dimensional (3-D) radar imaging is one of the challenging problems in radar imaging enhancement. The existing sparsity regularizations are limited to the heavy computational burden and time-consuming iteration operation. Compared with the conventional sparsity regularizations, the super-resolution (SR) imaging methods based on convolution neural network (CNN) can promote imaging time and achieve more accuracy. However, they are confined to 2-D space and model training under small dataset is not competently considered. To solve these problem, a fast and high-quality 3-D terahertz radar imaging method based on lightweight super-resolution CNN (SR-CNN) is proposed in this paper. First, an original 3-D radar echo model is presented and the expected SR model is derived by the given imaging geometry. Second, the SR imaging method based on lightweight SR-CNN is proposed to improve the image quality and speed up the imaging time. Furthermore, the resolution characteristics among spectrum estimation, sparsity regularization and SR-CNN are analyzed by the point spread function (PSF). Finally, electromagnetic computation simulations are carried out to validate the effectiveness of the proposed method in terms of image quality. The robustness against noise and the stability under small are demonstrate by ablation experiments.

scholarly journals Diffuse Optical Imaging: Safe and Functional Medical Imaging Technique

2018 ◽  
Vol 1 (2) ◽  
pp. 1-4
Author(s):  
Omnia Hamdy ◽  
Mahmoud F. Hassan ◽  
Nahed H. Solouma ◽  
Nahed H. Solouma
Keyword(s):  
Medical Imaging ◽  
Optical Imaging ◽  
Imaging Technique ◽  
Medical Applications ◽  
Imaging Method ◽  
Diffuse Optical Imaging ◽  
Optical Imaging Technique

Optical imaging method provides safe and encouraging tool in many medical applications. In this editorial, principle operation, instrumentation, medical applications and advantages of diffuse optical imaging technique are presented and discussed.

scholarly journals Sinusoidal Single-Pixel Imaging Based on Fourier Positive–Negative Intensity Correlation

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1674 ◽  
Author(s):  
Ling-Tong Meng ◽  
Ping Jia ◽  
Hong-Hai Shen ◽  
Ming-Jie Sun ◽  
Dong Yao ◽  
...  
Keyword(s):  
Imaging Technique ◽  
Imaging Techniques ◽  
Light Illumination ◽  
Pixel Detector ◽  
Time Dimension ◽  
High Quality ◽  
Intensity Correlation ◽  
Pixel Detectors ◽  
Fourier Matrix ◽  
Single Pixel

Single-pixel imaging techniques extend the time dimension to reconstruct a target scene in the spatial domain based on single-pixel detectors. Structured light illumination modulates the target scene by utilizing multi-pattern projection, and the reflected or transmitted light is measured by a single-pixel detector as total intensity. To reduce the imaging time and capture high-quality images with a single-pixel imaging technique, orthogonal patterns have been used instead of random patterns in recent years. The most representative among them are Hadamard patterns and Fourier sinusoidal patterns. Here, we present an alternative Fourier single-pixel imaging technique that can reconstruct high-quality images with an intensity correlation algorithm using acquired Fourier positive–negative images. We use the Fourier matrix to generate sinusoidal and phase-shifting sinusoid-modulated structural illumination patterns, which correspond to Fourier negative imaging and positive imaging, respectively. The proposed technique can obtain two centrosymmetric images in the intermediate imaging course. A high-quality image is reconstructed by applying intensity correlation to the negative and positive images for phase compensation. We performed simulations and experiments, which obtained high-quality images, demonstrating the feasibility of the methods. The proposed technique has the potential to image under sub-sampling conditions.

Image Quality Analysis of X-ray Grating Interferometer Using Integrating-bucket Method

2020 ◽  
Vol 64 (2) ◽  
pp. 20503-1-20503-5
Author(s):  
Faiz Wali ◽  
Shenghao Wang ◽  
Ji Li ◽  
Jianheng Huang ◽  
Yaohu Lei ◽  
...  
Keyword(s):  
Image Quality ◽  
Phase Contrast ◽  
Quality Analysis ◽  
Phase Contrast Imaging ◽  
Phase Image ◽  
Contrast Imaging ◽  
High Quality ◽  
Image Quality Analysis ◽  
X Ray ◽  
Grating Interferometer

Abstract Grating-based x-ray phase-contrast imaging has the potential to enhance image quality and provide inner structure details non-destructively. In this work, using grating-based x-ray phase-contrast imaging system and employing integrating-bucket method, the quantitative expressions of signal-to-noise ratios due to photon statistics and mechanical error are analyzed in detail. Photon statistical noise and mechanical error are the main sources affecting the image noise in x-ray grating interferometry. Integrating-bucket method is a new phase extraction method translated to x-ray grating interferometry; hence, its image quality analysis would be of great importance to get high-quality phase image. The authors’ conclusions provide an alternate method to get high-quality refraction signal using grating interferometer, and hence increases applicability of grating interferometry in preclinical and clinical usage.

scholarly journals An All-Batch Loss for Constructing Prediction Intervals

2021 ◽  
Vol 11 (4) ◽  
pp. 1728
Author(s):  
Hua Zhong ◽  
Li Xu
Keyword(s):  
Gradient Descent ◽  
State Of The Art ◽  
Prediction Interval ◽  
Feedforward Neural Networks ◽  
Important Research ◽  
Likelihood Principle ◽  
High Quality ◽  
Construction Methods ◽  
Important Research Topic ◽  
Benchmark Datasets

The prediction interval (PI) is an important research topic in reliability analyses and decision support systems. Data size and computation costs are two of the issues which may hamper the construction of PIs. This paper proposes an all-batch (AB) loss function for constructing high quality PIs. Taking the full advantage of the likelihood principle, the proposed loss makes it possible to train PI generation models using the gradient descent (GD) method for both small and large batches of samples. With the structure of dual feedforward neural networks (FNNs), a high-quality PI generation framework is introduced, which can be adapted to a variety of problems including regression analysis. Numerical experiments were conducted on the benchmark datasets; the results show that higher-quality PIs were achieved using the proposed scheme. Its reliability and stability were also verified in comparison with various state-of-the-art PI construction methods.

scholarly journals An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

2021 ◽  
Vol 9 (7) ◽  
pp. 691
Author(s):  
Kai Hu ◽  
Yanwen Zhang ◽  
Chenghang Weng ◽  
Pengsheng Wang ◽  
Zhiliang Deng ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Quality Evaluation ◽  
High Efficiency ◽  
Natural Image ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Image Quality Evaluation ◽  
Adversarial Network ◽  
Underwater Image

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.

High compressive ghost imaging method based on discrete cosine transform using weight coefficient matching

2019 ◽  
Vol 66 (17) ◽  
pp. 1736-1743 ◽  
Author(s):  
Huazheng Wu ◽  
Xiangfeng Meng ◽  
Yurong Wang ◽  
Yongkai Yin ◽  
Xiulun Yang ◽  
...  
Keyword(s):  
Discrete Cosine Transform ◽  
Weight Coefficient ◽  
Imaging Method ◽  
Ghost Imaging ◽  
Cosine Transform ◽  
Coefficient Matching
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