A Novel Sub-Image Local Area Minimum Entropy Reconstruction Method for HRWS SAR Adaptive Unambiguous Imaging

Multichannel high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is a vital technique for modern remote sensing. As multichannel SAR systems usually face the problem of azimuth nonuniform sampling resulting in azimuth ambiguity, the conventional reconstruction methods are adopted to obtain the uniformly sampled signal. However, various errors, especially amplitude, phase, and baseline errors, always significantly degrade the performance of the reconstruction methods. To solve this problem, in this paper, a novel sub-image local area minimum entropy reconstruction method (SILAMER) is proposed, which has favorable adaptability to the HRWS SAR system with various errors. First, according to the idea of image domain reconstruction, the sub-images are generated by employing the back-projection algorithm. Then, we proposed an estimation algorithm based on sub-image local area minimum entropy to obtain the optimal reconstruction coefficient and the compensation phase, which can greatly improve the estimation efficiency by using a local area of the sub-image as the input for estimation. Finally, the sub-images are weighted by the optimal estimated reconstruction coefficient and calibrated by the compensation phase to obtain the unambiguous reconstruction image. The experimental results verify the effectiveness of the proposed method. Noticeably, the proposed algorithm has two additional advantages, i.e., (1) it can perform well under the condition of low signal-to-noise ratio (SNR), and (2) it is suitable for the curved trajectory SAR reconstruction. The simulations verify these advantages of the proposed method.

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Self-Adaptive Image Reconstruction Inspired by Insect Compound Eye Mechanism

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/125321 ◽

2012 ◽

Vol 2012 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Jiahua Zhang ◽

Aiye Shi ◽

Xin Wang ◽

Linjie Bian ◽

Fengchen Huang ◽

...

Keyword(s):

Light Intensity ◽

Compound Eye ◽

Signal To Noise Ratio ◽

Processing System ◽

Reconstruction Method ◽

Sparse Model ◽

Reconstruction Methods ◽

Adaptive Reconstruction ◽

Distinct Block ◽

Block Effect

Inspired by the mechanism of imaging and adaptation to luminosity in insect compound eyes (ICE), we propose an ICE-based adaptive reconstruction method (ARM-ICE), which can adjust the sampling vision field of image according to the environment light intensity. The target scene can be compressive, sampled independently with multichannel through ARM-ICE. Meanwhile, ARM-ICE can regulate the visual field of sampling to control imaging according to the environment light intensity. Based on the compressed sensing joint sparse model (JSM-1), we establish an information processing system of ARM-ICE. The simulation of a four-channel ARM-ICE system shows that the new method improves the peak signal-to-noise ratio (PSNR) and resolution of the reconstructed target scene under two different cases of light intensity. Furthermore, there is no distinct block effect in the result, and the edge of the reconstructed image is smoother than that obtained by the other two reconstruction methods in this work.

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Parameter estimation under non-stationary circumstances using extended signal model

Advances in Radio Science ◽

10.5194/ars-12-43-2014 ◽

2014 ◽

Vol 12 ◽

pp. 43-48

Author(s):

M. Lechtenberg ◽

J. Götze ◽

K. Görner ◽

C. Rehtanz

Keyword(s):

Fundamental System ◽

Estimation Algorithm ◽

Projection Algorithm ◽

Back Projection ◽

Short Term ◽

Signal Model ◽

Model Order ◽

Amplitude Estimation ◽

Sampled Signal ◽

Electro Magnetic

Abstract. In the context of transient processes in power system measurements, a sampled signal is analyzed with respect to electro-magnetic influences. These are most likely superposed sinusoids that can be found next to the fundamental system sinusoid. However, such signal is not stationary, i.e. has a varying model order which means that temporary and exponentially damped sinusoids appear eventually. In our previous work, the exponential damping was only considered indirectly but not explicitly incorporated in the signal model used. Regarding the amplitude estimation, this led to inaccuracies since the amplitude was incorrectly assumed constant within a window of samples. In this paper, we use ESPRIT's ability to also estimate a parameter of exponential damping, improve the corresponding signal model, advance the amplitude estimation algorithm and back projection algorithm. Especially short-term signal components can be tracked far more precise.

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SARA-GAN: Self-Attention and Relative Average Discriminator Based Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Frontiers in Neuroinformatics ◽

10.3389/fninf.2020.611666 ◽

2020 ◽

Vol 14 ◽

Cited By ~ 1

Author(s):

Zhenmou Yuan ◽

Mingfeng Jiang ◽

Yaming Wang ◽

Bo Wei ◽

Yongming Li ◽

...

Keyword(s):

Signal To Noise Ratio ◽

Similarity Index ◽

Structural Similarity ◽

Attention Mechanism ◽

Generative Adversarial Networks ◽

Reconstruction Method ◽

Mri Imaging ◽

Adversarial Networks ◽

Reconstruction Methods ◽

Mri Reconstruction

Research on undersampled magnetic resonance image (MRI) reconstruction can increase the speed of MRI imaging and reduce patient suffering. In this paper, an undersampled MRI reconstruction method based on Generative Adversarial Networks with the Self-Attention mechanism and the Relative Average discriminator (SARA-GAN) is proposed. In our SARA-GAN, the relative average discriminator theory is applied to make full use of the prior knowledge, in which half of the input data of the discriminator is true and half is fake. At the same time, a self-attention mechanism is incorporated into the high-layer of the generator to build long-range dependence of the image, which can overcome the problem of limited convolution kernel size. Besides, spectral normalization is employed to stabilize the training process. Compared with three widely used GAN-based MRI reconstruction methods, i.e., DAGAN, DAWGAN, and DAWGAN-GP, the proposed method can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measure(SSIM), and the details of the reconstructed image are more abundant and more realistic for further clinical scrutinization and diagnostic tasks.

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An Azimuth Signal-Reconstruction Method Based on Two-Step Projection Technology for Spaceborne Azimuth Multi-Channel High-Resolution and Wide-Swath SAR

Remote Sensing ◽

10.3390/rs13244988 ◽

2021 ◽

Vol 13 (24) ◽

pp. 4988

Author(s):

Ning Li ◽

Hanqing Zhang ◽

Jianhui Zhao ◽

Lin Wu ◽

Zhengwei Guo

Keyword(s):

High Resolution ◽

Signal To Noise Ratio ◽

Signal Reconstruction ◽

Simulated Data ◽

Reconstruction Algorithm ◽

Reconstruction Method ◽

Signal Ratio ◽

Translation Invariant ◽

Sampled Signal ◽

Wide Swath

Azimuth non-uniform signal-reconstruction is a critical step for azimuth multi-channel high-resolution wide-swath (HRWS) synthetic aperture radar (SAR) data processing. However, the received non-uniform signal has noise in the actual azimuth multi-channel SAR (MCSAR) operation, which leads to the serious reduction in the signal-to-noise ratio (SNR) of the results processed by a traditional reconstruction algorithm. Aiming to address the problem of reducing the SNR of the traditional reconstruction algorithm in the reconstruction of non-uniform signal with noise, a novel signal-reconstruction algorithm based on two-step projection technology (TSPT) for the MCSAR system is proposed in this paper. The key part of the TSPT algorithm consists of a two-step projection. The first projection is to project the given signal into the selected intermediate subspace, spanned by the integer conversion of the compact support kernel function. This process generates a set of sparse equations, which can be solved efficiently by using the sparse equation solver. The second key projection is to project the first projection result into the subspace of the known sampled signal. The secondary projection can be achieved with a digital linear translation invariant (LSI) filter and generate a uniformly spaced signal. As a result, compared with the traditional azimuth MCSAR signal-reconstruction algorithm, the proposed algorithm can improve SNR and reduce the azimuth ambiguity-signal-ratio (AASR). The processing results of simulated data and real raw data verify the effectiveness of the proposed algorithm.

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A New Method for Superresolution Image Reconstruction Based on Surveying Adjustment

Journal of Nanomaterials ◽

10.1155/2014/931616 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Jianjun Zhu ◽

Cui Zhou ◽

Donghao Fan ◽

Jinghong Zhou

Keyword(s):

Image Reconstruction ◽

Convex Sets ◽

Signal To Noise Ratio ◽

Main Idea ◽

New Method ◽

Projection Algorithm ◽

Reconstruction Method ◽

Reconstruction Process ◽

Adjustment Method ◽

Image Reconstruction Method

A new method for superresolution image reconstruction based on surveying adjustment method is described in this paper. The main idea of such new method is that a sequence of low-resolution images are taken firstly as observations, and then observation equations are established for the superresolution image reconstruction. The gray function of the object surface can be found by using surveying adjustment method from the observation equations. High-resolution pixel value of the corresponding area can be calculated by using the gray function. The results show that the proposed algorithm converges much faster than that of conventional superresolution image reconstruction method. By using the new method, the visual feeling of reconstructed image can be greatly improved compared to that of iterative back projection algorithm, and its peak signal-to-noise ratio can also be improved by nearly 1 dB higher than the projection onto convex sets algorithm. Furthermore, this method can successfully avoid the ill-posed problems in reconstruction process.

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High-Resolution Ultrasound Imaging Enabled by Random Interference and Joint Image Reconstruction

Sensors ◽

10.3390/s20226434 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6434

Author(s):

Pavel Ni ◽

Heung-No Lee

Keyword(s):

High Resolution ◽

Image Reconstruction ◽

Ultrasound Imaging ◽

Signal To Noise Ratio ◽

Reconstruction Method ◽

Signal To Noise ◽

Joint Reconstruction ◽

Reconstruction Scheme ◽

Reconstruction Methods ◽

Noise Ratio

In ultrasound, wave interference is an undesirable effect that degrades the resolution of the images. We have recently shown that a wavefront of random interference can be used to reconstruct high-resolution ultrasound images. In this study, we further improve the resolution of interference-based ultrasound imaging by proposing a joint image reconstruction scheme. The proposed reconstruction scheme utilizes radio frequency (RF) signals from all elements of the sensor array in a joint optimization problem to directly reconstruct the final high-resolution image. By jointly processing array signals, we significantly improved the resolution of interference-based imaging. We compare the proposed joint reconstruction method with popular beamforming techniques and the previously proposed interference-based compound method. The simulation study suggests that, among the different reconstruction methods, the joint reconstruction method has the lowest mean-squared error (MSE), the best peak signal-to-noise ratio (PSNR), and the best signal-to-noise ratio (SNR). Similarly, the joint reconstruction method has an exceptional structural similarity index (SSIM) of 0.998. Experimental studies showed that the quality of images significantly improved when compared to other image reconstruction methods. Furthermore, we share our simulation codes as an open-source repository in support of reproducible research.

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Global Inhomogeneity Index Evaluation of a DCT-based EIT Lung Imaging

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3010 ◽

2020 ◽

Vol 6 (3) ◽

pp. 36-39

Author(s):

Rongqing Chen ◽

Knut Möller

Keyword(s):

Classical Method ◽

Lung Imaging ◽

Imaging Method ◽

Reconstruction Method ◽

Essential Information ◽

Preliminary Results ◽

Reconstruction Methods ◽

Comparison Results ◽

Index Evaluation ◽

Inhomogeneity Index

AbstractPurpose: To evaluate a novel structural-functional DCT-based EIT lung imaging method against the classical EIT reconstruction. Method: Taken retrospectively from a former study, EIT data was evaluated using both reconstruction methods. For different phases of ventilation, EIT images are analyzed with respect to the global inhomogeneity (GI) index for comparison. Results: A significant less variant GI index was observed in the DCTbased method, compared to the index from classical method. Conclusion: The DCT-based method generates more accurate lung contour yet decreasing the essential information in the image which affects the GI index. These preliminary results must be consolidated with more patient data in different breathing states.

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A Novel Symbol Rate Estimation Algorithm for Phase Modulating Signals in Wireless Communications

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.443.392 ◽

2013 ◽

Vol 443 ◽

pp. 392-396

Author(s):

Peng Zhou ◽

Chi Sheng Li

Keyword(s):

Signal To Noise Ratio ◽

Estimation Algorithm ◽

Frequency Resolution ◽

Phase Shift Keying ◽

Phase Jump ◽

Rate Estimation ◽

Awgn Channel ◽

Symbol Rate ◽

Shift Keying ◽

Simulation Results

In this paper, we proposed a new symbol rate estimation algorithm for phase shift keying (PSK) and qua drawtube amplitude modulation (QAM) signals in AWGN channel First we constructe a delay-multiplied signal, from which we obtaine the modulated information. Then we calculated the instantaneous autocorrelation of the delay-multiplied signal to pick out the phase jump. To eliminate the restriction of frequency resolution in fast Fourier transform, we performed a Chirp-Z transform to find out the exact spectral line which represente the symbol rate of the signal to be analyzed. Compared with the existing algorithms, it is a simple solution that has a better performance and accuracy in low signal-to-noise-ratio channel conditions. Simulation results show that the probability of relative estimating deviation below 0.1% reaches 100% and the average and standard variance of absolute estimation deviation are at the magnitude of 10-2 when SNR is over 2dB.

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Novel Reconstruction Using Roux-en-Y Plus Antral Obstruction Following Proximal Gastrectomy: Feasibility and Quality of Life Benefits

10.21203/rs.2.11422/v1 ◽

2019 ◽

Author(s):

Dejun Yang ◽

Changming Wang ◽

Hongbing Fu ◽

Ziran Wei ◽

Xin Zhang ◽

...

Keyword(s):

Quality Of Life ◽

Reflux Esophagitis ◽

Proximal Gastrectomy ◽

Partial Gastrectomy ◽

Incidence Rates ◽

Reconstruction Method ◽

Reflux Symptoms ◽

Reconstruction Methods ◽

Postoperative Reflux

Abstract Background and Aims Routine gastroesophagostomy has been shown to have adverse effects on the recovery of digestive functions and quality of life because patients typically experience reflux symptoms after proximal gastrectomy. This study was performed to assess the feasibility and quality of life benefits of a novel reconstruction method termed Roux-en-Y anastomosis plus antral obstruction (RYAO) following proximal partial gastrectomy. Methods A total of 73 patients who underwent proximal gastrectomy from June 2015 to June 2017 were divided into two groups according to digestive reconstruction methods [RYAO (37 patients) and conventional esophagogastric anastomosis with pyloroplasty (EGPP, 36 patients)]. Clinical data were compared between the two groups retrospectively. Results The mean operative time for digestive reconstruction was slightly longer in the RYAO group than in the EGPP group. However, the incidence of postoperative short-term complications did not differ between the RYAO and the EGPP groups. At the 6-month follow-up, the incidence rates of both reflux esophagitis and gastritis were lower in the RYAO group than in the EGPP group (P = 0.002). Additionally, body weight recovery was better in the RYAO group (P = 0.028). The scale tests indicated that compared with the patients in the EGPP group, the patients in the RYAO group had significantly reduced reflux, nausea and vomiting and reported improvements in their overall health status and quality of life (all P < 0.05). Conclusion RYAO reconstruction may be a feasible procedure to reduce postoperative reflux symptoms and the incidence of reflux esophagitis and gastritis, thus improving patient quality of life after proximal gastrectomy.

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Minimum Array Elements for Resolution of Several Direction of Arrival Estimation Methods in Various Noise-Level Environments

Journal of Telecommunications and Information Technology ◽

10.26636/jtit.2018.119417 ◽

2018 ◽

Vol 2 ◽

pp. 87-94

Author(s):

Ismail El Ouargui ◽

Said Safi ◽

Miloud Frikel

Keyword(s):

Gaussian Noise ◽

Noise Level ◽

Signal To Noise Ratio ◽

Additive White Gaussian Noise ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Estimation Methods ◽

Minimum Number ◽

Informative Signals

The resolution of a Direction of Arrival (DOA) estimation algorithm is determined based on its capability to resolve two closely spaced signals. In this paper, authors present and discuss the minimum number of array elements needed for the resolution of nearby sources in several DOA estimation methods. In the real world, the informative signals are corrupted by Additive White Gaussian Noise (AWGN). Thus, a higher signal-to-noise ratio (SNR) oﬀers a better resolution. Therefore, we show the performance of each method by applying the algorithms in diﬀerent noise level environments.

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