scholarly journals 140 GHz Ultra-Long Bessel–Like Beam with Near-Wavelength Beamwidth

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6791
Author(s):  
Gyeongsik Ok ◽  
Kee Jai Park
Keyword(s):  
Imaging Technique ◽  
Super Resolution ◽  
Incident Beam ◽  
Thz Imaging ◽  
Depth Of Focus ◽  
Beam Angle ◽  
Sensing Applications ◽  
Nondestructive Imaging ◽  
Wave Range ◽  
Wall Imaging

The Bessel–Gauss beam has outstanding features, such as long depth of focus (DOF) and super resolution for nondestructive imaging inspection. However, most approaches for generating a nondiffractive beam have mainly focused on extending the DOF. In this study, the ultra-long high-resolution Bessel–like beam was first demonstrated in a sub-THz wave range (140 GHz). An axicon lens having an apex angle of 110° was used to generate the highly focused Bessel–like beam. To extend the depth of focus, we varied the incident beam angle on the axicon by moving the first lens distance. With the newly developed beam profiler, 3D beam profiles were acquired for characterizing in detail the beam propagation. As a result, even if the depth of focus was 72 times (154 mm) the source wavelength (2.143 mm), the focusing beamwidth was simultaneously maintained at 1.4 times (3.0 mm) the wavelength (i.e., the near-wavelength beamwidth). An ultra-long needle beam of near-wavelength size can promote the applicability of the sub-THz imaging technique in noninvasive sensing applications, such as computer tomography, materials inspection, and through-the-wall-imaging.

S-DolLion-MSVNN: A Hybrid Model for Developing the Super-Resolution Image From the Multispectral Satellite Image

2020 ◽  
Author(s):  
Anil B Gavade ◽  
Vijay S Rajpurohit
Keyword(s):  
Hybrid Model ◽  
Satellite Image ◽  
Optimal Solution ◽  
Super Resolution ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Resolution Image ◽  
Sensing Applications ◽  
Multispectral Satellite Image ◽  
Lion Optimization Algorithm

Abstract Super-resolution offers a new image with high resolution from the low-resolution (LR) image that is highly employed for the numerous remote sensing applications. Most of the existing techniques for formation of the super-resolution image exhibit the loss of quality and deviation from the original multi-spectral LR image. Thus, this paper aims at proposing an efficient super-resolution method using the hybrid model. The hybrid model is developed using the support vector regression model and multi-support vector neural network (MSVNN), and the weights of the MSVNN is tuned optimally using the proposed algorithm. The proposed DolLion algorithm is the integration of the dolphin echolocation algorithm and lion optimization algorithm that exhibits better convergence and offers a global optimal solution. The experimentation is performed using the datasets taken from the multi-spectral scene images. The optimal and effective formation of the super-resolution image using the proposed hybrid model outperforms the existing methods, and the analysis using the second-derivative-like measure of enhancement (SDME) ensures that the proposed method is better and yields a maximum SDME of 67.6755 dB.

scholarly journals On super-resolution imaging as a multiparameter estimation problem

2017 ◽  
Vol 15 (08) ◽  
pp. 1740005 ◽  
Author(s):  
Andrzej Chrostowski ◽  
Rafał Demkowicz-Dobrzański ◽  
Marcin Jarzyna ◽  
Konrad Banaszek
Keyword(s):  
Phase Shift ◽  
Light Source ◽  
Imaging Technique ◽  
Super Resolution ◽  
Estimation Problem ◽  
Joint Estimation ◽  
Spatial Extent ◽  
Resolution Imaging

We consider the problem of characterizing the spatial extent of a composite light source using the super-resolution imaging technique based on mode demultiplexing when the centroid of the source is not known precisely. We show that the essential features of this problem can be mapped onto a simple qubit model for joint estimation of a phase shift and a dephasing strength.

scholarly journals Super-Resolution Lensless Imaging of Cells Using Brownian Motion

2019 ◽  
Vol 9 (10) ◽  
pp. 2080
Author(s):  
Yuan Fang ◽  
Ningmei Yu ◽  
Yuquan Jiang
Keyword(s):  
Brownian Motion ◽  
Imaging System ◽  
Imaging Technique ◽  
Super Resolution ◽  
Image Sensor ◽  
Oxide Semiconductor ◽  
Objective Lens ◽  
Cell Detection ◽  
Lensless Imaging ◽  
Cell Image

The lensless imaging technique, which integrates a microscope into a complementary metal oxide semiconductor (CMOS) digital image sensor, has become increasingly important for the miniaturization of biological microscope and cell detection equipment. However, limited by the pixel size of the CMOS image sensor (CIS), the resolution of a cell image without optical amplification is low. This is also a key defect with the lensless imaging technique, which has been studied by a many scholars. In this manuscript, we propose a method to improve the resolution of the cell images using the Brownian motion of living cells in liquid. A two-step algorithm of motion estimation for image registration is proposed. Then, the raw holographic images are reconstructed using normalized convolution super-resolution algorithm. The result shows that the effect of the collected cell image under the lensless imaging system is close to the effect of a 10× objective lens.

scholarly journals A Super-Resolution and Fusion Approach to Enhancing Hyperspectral Images

2018 ◽  
Vol 10 (9) ◽  
pp. 1416 ◽  
Author(s):  
Chiman Kwan ◽  
Joon Choi ◽  
Stanley Chan ◽  
Jin Zhou ◽  
Bence Budavari
Keyword(s):  
Surface Characterization ◽  
Performance Metrics ◽  
Color Image ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Vegetation Monitoring ◽  
Color Mapping ◽  
Sensing Applications ◽  
Single Image Super Resolution ◽  
Spectral Ranges

High resolution (HR) hyperspectral (HS) images have found widespread applications in terrestrial remote sensing applications, including vegetation monitoring, military surveillance and reconnaissance, fire damage assessment, and many others. They also find applications in planetary missions such as Mars surface characterization. However, resolutions of most HS imagers are limited to tens of meters. Existing resolution enhancement techniques either require additional multispectral (MS) band images or use a panchromatic (pan) band image. The former poses hardware challenges, whereas the latter may have limited performance. In this paper, we present a new resolution enhancement algorithm for HS images that only requires an HR color image and a low resolution (LR) HS image cube. Our approach integrates two newly developed techniques: (1) A hybrid color mapping (HCM) algorithm, and (2) A Plug-and-Play algorithm for single image super-resolution. Comprehensive experiments (objective (five performance metrics), subjective (synthesized fused images in multiple spectral ranges), and pixel clustering) using real HS images and comparative studies with 20 representative algorithms in the literature were conducted to validate and evaluate the proposed method. Results demonstrated that the new algorithm is very promising.

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