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.

Generating High-Quality Air-Coupled Ultrasonic Images for Wooden Material Characterization by Single Image Super-Resolution

2019 ◽  
Vol 34 (03) ◽  
pp. 2054008
Author(s):  
Lujun Lin ◽  
Yiming Fang ◽  
Xiaochen Du ◽  
Zhu Zhou
Keyword(s):  
Image Quality ◽  
Super Resolution ◽  
Visual Assessment ◽  
Wood Products ◽  
Superior Performance ◽  
Single Image ◽  
High Quality ◽  
Practical Applications ◽  
Image Super Resolution ◽  
Single Image Super Resolution

As the practical applications in other fields, high-resolution images are usually expected to provide a more accurate assessment for the air-coupled ultrasonic (ACU) characterization of wooden materials. This paper investigated the feasibility of applying single image super-resolution (SISR) methods to recover high-quality ACU images from the raw observations that were constructed directly by the on-the-shelf ACU scanners. Four state-of-the-art SISR methods were applied to the low-resolution ACU images of wood products. The reconstructed images were evaluated by visual assessment and objective image quality metrics, including peak signal-to-noise-ratio and structural similarity. Both qualitative and quantitative evaluations indicated that the substantial improvement of image quality can be yielded. The results of the experiments demonstrated the superior performance and high reproducibility of the method for generating high-quality ACU images. Sparse coding based super-resolution and super-resolution convolutional neural network (SRCNN) significantly outperformed other algorithms. SRCNN has the potential to act as an effective tool to generate higher resolution ACU images due to its flexibility.

scholarly journals A Novel 3D Imaging Method for Airborne Downward-Looking Sparse Array SAR Based on Special Squint Model

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaozhen Ren ◽  
Yao Qin ◽  
Lihong Qiao
Keyword(s):  
3D Imaging ◽  
Three Dimensional ◽  
Imaging Method ◽  
Computationally Efficient ◽  
Theory Analysis ◽  
Imaging Geometry ◽  
Range Resolution ◽  
Sparse Array ◽  
Resolution Imaging ◽  
Cross Track

Three-dimensional (3D) imaging technology based on antenna array is one of the most important 3D synthetic aperture radar (SAR) high resolution imaging modes. In this paper, a novel 3D imaging method is proposed for airborne down-looking sparse array SAR based on the imaging geometry and the characteristic of echo signal. The key point of the proposed algorithm is the introduction of a special squint model in cross track processing to obtain accurate focusing. In this special squint model, point targets with different cross track positions have different squint angles at the same range resolution cell, which is different from the conventional squint SAR. However, after theory analysis and formulation deduction, the imaging procedure can be processed with the uniform reference function, and the phase compensation factors and algorithm realization procedure are demonstrated in detail. As the method requires only Fourier transform and multiplications and thus avoids interpolations, it is computationally efficient. Simulations with point scatterers are used to validate the method.

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 Intra-operative OCT (iOCT) Image Quality Enhancement: A Super-Resolution Approach Using High Quality iOCT 3D Scans

2021 ◽  
pp. 21-31
Author(s):  
Charalampos Komninos ◽  
Theodoros Pissas ◽  
Blanca Flores ◽  
Edward Bloch ◽  
Tom Vercauteren ◽  
...  
Keyword(s):  
Image Quality ◽  
Super Resolution ◽  
Quality Enhancement ◽  
High Quality ◽  
Image Quality Enhancement ◽  
3D Scans

scholarly journals Three-Dimensional Imaging Method for Array ISAR Based on Sparse Bayesian Inference

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3563
Author(s):  
Zekun Jiao ◽  
Chibiao Ding ◽  
Longyong Chen ◽  
Fubo Zhang
Keyword(s):  
Bayesian Inference ◽  
3D Imaging ◽  
Three Dimensional ◽  
Super Resolution ◽  
Information Criterion ◽  
Radar Data ◽  
Imaging Method ◽  
Model Order Selection ◽  
Imaging Model ◽  
Imaging Performance

The problem of synthesis scatterers in inverse synthetic aperture radar (ISAR) make it difficult to realize high-resolution three-dimensional (3D) imaging. Radar array provides an available solution to this problem, but the resolution is restricted by limited aperture size and number of antennas, leading to deterioration of the 3D imaging performance. To solve these problems, we propose a novel 3D imaging method with an array ISAR system based on sparse Bayesian inference. First, the 3D imaging model using a sparse linear array is introduced. Then the elastic net estimation and Bayesian information criterion are introduced to fulfill model order selection automatically. Finally, the sparse Bayesian inference is adopted to realize super-resolution imaging and to get the 3D image of target of interest. The proposed method is used to process real radar data of a Ku band array ISAR system. The results show that the proposed method can effectively solve the problem of synthesis scatterers and realize super-resolution 3D imaging, which verify the practicality of our proposed method.

scholarly journals Time-Frequency Reversion-Based Spectrum Analysis Method and Its Applications in Radar Imaging

2021 ◽  
Vol 13 (4) ◽  
pp. 600
Author(s):  
Jixiang Fu ◽  
Mengdao Xing ◽  
Guangcai Sun
Keyword(s):  
Spectrum Analysis ◽  
Near Field ◽  
Radar Imaging ◽  
Radar Signal ◽  
Imaging Method ◽  
Imaging Geometry ◽  
Time Frequency ◽  
Isar Imaging ◽  
Low Efficiency ◽  
Imaging Algorithms

Spectrum analysis (SA) plays an important role in radar signal processing, especially in radar imaging algorithm design. Because it is usually hard to obtain the analytical expression of spectrum by the Fourier integral directly, principle of stationary phase (POSP)-based SA is applied to approximate this integral. However, POSP requires the phase of the signal to vary rapidly, which is not the case in circular synthetic aperture radar (SAR) and turntable inverse SAR (ISAR). To solve this problem, a new SA method based on time-frequency reversion (TFRSA) is proposed, which utilizes the relationship of the Fourier transform pairs and their corresponding signal phases. In addition, the connection between the imaging geometry and time-frequency relationship is also analyzed and utilized to help solve the time-frequency reversion. When the TFRSA is applied to the linear trajectory SAR, the obtained spectrum expression is the same as the result of POSP. When it is applied to ISAR, the spectrum expressions of near-field and far-field are derived and their difference is found to be position-independent. Based on this finding, an extended polar format algorithm (EPFA) for near-field ISAR imaging is proposed, which can solve the distortion and defocusing problems caused by traditional ISAR imaging algorithms. When it is applied to the circular SAR (CSAR), a new and efficient imaging method based on EPFA is proposed, which can solve the low efficiency problem of conventional BP-based CSAR imaging algorithms. The simulations and real data processing results are provided to validate the effectiveness of proposed method.

scholarly journals TomoSAR 3D Reconstruction for Buildings Using Very Few Tracks of Observation: A Conditional Generative Adversarial Network Approach

2021 ◽  
Vol 13 (24) ◽  
pp. 5055
Author(s):  
Shihong Wang ◽  
Jiayi Guo ◽  
Yueting Zhang ◽  
Yuxin Hu ◽  
Chibiao Ding ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Super Resolution ◽  
Quality Data ◽  
Sar Images ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
High Quality Result

SAR tomography (TomoSAR) is an important technology for three-dimensional (3D) reconstruction of buildings through multiple coherent SAR images. In order to obtain sufficient signal-to-noise ratio (SNR), typical TomoSAR applications often require dozens of scenes of SAR images. However, limited by time and cost, the available SAR images are often only 3–5 scenes in practice, which makes the traditional TomoSAR technique unable to produce satisfactory SNR and elevation resolution. To tackle this problem, the conditional generative adversarial network (CGAN) is proposed to improve the TomoSAR 3D reconstruction by learning the prior information of building. Moreover, the number of tracks required can be reduced to three. Firstly, a TomoSAR 3D super-resolution dataset is constructed using high-quality data from the airborne array and low-quality data obtained from a small amount of tracks sampled from all observations. Then, the CGAN model is trained to estimate the corresponding high-quality result from the low-quality input. Airborne data experiments prove that the reconstruction results are improved in areas with and without overlap, both qualitatively and quantitatively. Furthermore, the network pretrained on the airborne dataset is directly used to process the spaceborne dataset without any tuning, and generates satisfactory results, proving the effectiveness and robustness of our method. The comparative experiment with nonlocal algorithm also shows that the proposed method has better height estimation and higher time efficiency.

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