A high resolution ISAR imaging method base on image fusion with low SNR

Author(s):  
M. Lv ◽  
F. Xu ◽  
J. Ma ◽  
L. Chen ◽  
H. Chen
IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 110651-110659
Author(s):  
Jiyuan Chen ◽  
Letao Xu ◽  
Xiaoyi Pan ◽  
Pu Zheng ◽  
Shunping Xiao

2019 ◽  
Vol 30 (03) ◽  
pp. 492-503
Author(s):  
Long Xiang ◽  
◽  
Shaodong Li ◽  
Jun Yang ◽  
Wenfang Chen ◽  
...  

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 140499-140512 ◽  
Author(s):  
Li Li ◽  
Li Yan ◽  
Dong Li ◽  
Hongqing Liu ◽  
Chengxiang Zhang

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4409 ◽  
Author(s):  
Zhiping Yin ◽  
Xinfei Lu ◽  
Weidong Chen

A new CS-based inverse synthetic aperture radar (ISAR) imaging framework is proposed to enhance both the image performance and the robustness at a low SNR. An ISAR echo preprocessing method for enhancing the ISAR imaging quality of compressed sensing (CS) based algorithms is developed by implementing matched filtering, echo denoising and matrix optimization sequentially. After the preprocessing, the two-dimensional (2D) SL0 algorithm is applied to reconstruct an ISAR image in the range and cross-range plane through a series of 2D matrices using the 2D CS theory, rather than converting the 2D convex optimization problem to the one-dimensional (1D) problem in the image reconstruction process. The proposed preprocessing framework is verified by simulations and experiment. Simulations and experimental results show that the ISAR image obtained by the 2D sparse recovery algorithm with our proposed method has a better performance.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Tiantian Feng ◽  
Lixin Guo

A novel multiview inverse synthetic aperture radar (ISAR) imaging method is proposed to simulate high-resolution and identifiable ISAR image of complex targets by handling large-angle and wide-bandwidth scattering data. The scattering data are simulated with the shooting and bouncing ray (SBR) method. The bidirectional ray-tracing algorithm is developed to reduce the computation time. Simulation results indicate that the improved method is efficient and reliable to calculate electromagnetic (EM) scattering of electrically large targets. To implement the multiview ISAR imaging method after data simulation, we divide the large-angle and wide-bandwidth scattering data into subaperture data and conduct ISAR image processing for each subaperture datum locally. Transforming each subaperture ISAR image to the global coordinate system and summing them together will produce the high-resolution ISAR image that is meaningful for the database set up for synthetic aperture radar automatic target recognition (SAR ATR). The final simulation ISAR images further validate the great performance of our scattering calculation algorithm and ISAR imaging method.


BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Sergio Gabarre ◽  
Frank Vernaillen ◽  
Pieter Baatsen ◽  
Katlijn Vints ◽  
Christopher Cawthorne ◽  
...  

Abstract Background Array tomography (AT) is a high-resolution imaging method to resolve fine details at the organelle level and has the advantage that it can provide 3D volumes to show the tissue context. AT can be carried out in a correlative way, combing light and electron microscopy (LM, EM) techniques. However, the correlation between modalities can be a challenge and delineating specific regions of interest in consecutive sections can be time-consuming. Integrated light and electron microscopes (iLEMs) offer the possibility to provide well-correlated images and may pose an ideal solution for correlative AT. Here, we report a workflow to automate navigation between regions of interest. Results We use a targeted approach that allows imaging specific tissue features, like organelles, cell processes, and nuclei at different scales to enable fast, directly correlated in situ AT using an integrated light and electron microscope (iLEM-AT). Our workflow is based on the detection of section boundaries on an initial transmitted light acquisition that serves as a reference space to compensate for changes in shape between sections, and we apply a stepwise refinement of localizations as the magnification increases from LM to EM. With minimal user interaction, this enables autonomous and speedy acquisition of regions containing cells and cellular organelles of interest correlated across different magnifications for LM and EM modalities, providing a more efficient way to obtain 3D images. We provide a proof of concept of our approach and the developed software tools using both Golgi neuronal impregnation staining and fluorescently labeled protein condensates in cells. Conclusions Our method facilitates tracing and reconstructing cellular structures over multiple sections, is targeted at high resolution ILEMs, and can be integrated into existing devices, both commercial and custom-built systems.


Author(s):  
Dioline Sara ◽  
Ajay Kumar Mandava ◽  
Arun Kumar ◽  
Shiny Duela ◽  
Anitha Jude

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