Optimization of Antenna Rotation Speed and Super-Resolution Imaging Based on Split Bregman Algorithm for circular Scan ISAR Systems

2021 ◽  
Author(s):  
Yanli Zhu ◽  
Peng Zhou ◽  
Zhenhua Zhang ◽  
Ying Wang ◽  
Xi Zhang
Keyword(s):  
Rotation Speed ◽  
Super Resolution ◽  
Split Bregman ◽  
Resolution Imaging ◽  
Split Bregman Algorithm

Wavelet Based Restoration of Images with Missing or Damaged Pixels

2011 ◽  
Vol 1 (2) ◽  
pp. 108-131 ◽  
Author(s):  
Hui Ji ◽  
Zuowei Shen ◽  
Yuhong Xu
Keyword(s):  
Image Restoration ◽  
Impulsive Noise ◽  
Image Inpainting ◽  
Super Resolution ◽  
Image Deblurring ◽  
Tight Frame ◽  
Split Bregman ◽  
Wide Range ◽  
Resolution Imaging ◽  
Degraded Images

AbstractThis paper addresses the problem of how to restore degraded images where the pixels have been partly lost during transmission or damaged by impulsive noise. A wide range of image restoration tasks is covered in the mathematical model considered in this paper - e.g. image deblurring, image inpainting and super-resolution imaging. Based on the assumption that natural images are likely to have a sparse representation in a wavelet tight frame domain, we propose a regularization-based approach to recover degraded images, by enforcing the analysis-based sparsity prior of images in a tight frame domain. The resulting minimization problem can be solved efficiently by the split Bregman method. Numerical experiments on various image restoration tasks - simultaneously image deblurring and inpainting, super-resolution imaging and image deblurring under impulsive noise - demonstrated the effectiveness of our proposed algorithm. It proved robust to mis-detection errors of missing or damaged pixels, and compared favorably to existing algorithms.

Multiple Aerosol Unmixing by the Split Bregman Algorithm

2011 ◽  
Author(s):  
Russell Warren ◽  
Stanley Osher ◽  
Richard Vanderbeek
Keyword(s):  
Split Bregman ◽  
Split Bregman Algorithm

scholarly journals Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Neural Network ◽  
Continuous Wave ◽  
Super Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Range Estimation ◽  
Resolution Image ◽  
Resolution Imaging

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

scholarly journals 3D super-resolved imaging in live cells using sub-diffractive plasmonic localization of hybrid nanopillar arrays

Nanophotonics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 2847-2859
Author(s):  
Soojung Kim ◽  
Hyerin Song ◽  
Heesang Ahn ◽  
Seung Won Jun ◽  
Seungchul Kim ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Imaging Techniques ◽  
Optical Loss ◽  
Super Resolution ◽  
Living Cells ◽  
Live Cells ◽  
Complex Biological System ◽  
Observation Volume ◽  
Resolution Imaging ◽  
Nanopillar Arrays

AbstractAnalysing dynamics of a single biomolecule using high-resolution imaging techniques has been had significant attentions to understand complex biological system. Among the many approaches, vertical nanopillar arrays in contact with the inside of cells have been reported as a one of useful imaging applications since an observation volume can be confined down to few-tens nanometre theoretically. However, the nanopillars experimentally are not able to obtain super-resolution imaging because their evanescent waves generate a high optical loss and a low signal-to-noise ratio. Also, conventional nanopillars have a limitation to yield 3D information because they do not concern field localization in z-axis. Here, we developed novel hybrid nanopillar arrays (HNPs) that consist of SiO2 nanopillars terminated with gold nanodisks, allowing extreme light localization. The electromagnetic field profiles of HNPs are obtained through simulations and imaging resolution of cell membrane and biomolecules in living cells are tested using one-photon and 3D multiphoton fluorescence microscopy, respectively. Consequently, HNPs present approximately 25 times enhanced intensity compared to controls and obtained an axial and lateral resolution of 110 and 210 nm of the intensities of fluorophores conjugated with biomolecules transported in living cells. These structures can be a great platform to analyse complex intracellular environment.

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