scholarly journals Research on spectral reconstruction algorithm for snapshot microlens array micro-hyperspectral imaging system

2021 ◽  
Author(s):  
Yu Changben ◽  
yang jin ◽  
mingjia wang ◽  
ci sun ◽  
Nan Song ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System ◽  
Reconstruction Algorithm ◽  
Microlens Array ◽  
Spectral Reconstruction

Ultrasonic Block Compressed Sensing Imaging Reconstruction Algorithm Based on Wavelet Sparse Representation

2020 ◽  
Vol 16 (3) ◽  
pp. 262-272
Author(s):  
Guangzhi Dai ◽  
Zhiyong He ◽  
Hongwei Sun
Keyword(s):  
Compressed Sensing ◽  
Sparse Representation ◽  
Linear Array ◽  
Imaging System ◽  
Ultrasonic Imaging ◽  
Large Data ◽  
Reconstruction Algorithm ◽  
Array Transducer ◽  
Total Data ◽  
Block Compressed Sensing

Background: This study is carried out targeting the problem of slow response time and performance degradation of imaging system caused by large data of medical ultrasonic imaging. In view of the advantages of CS, it is applied to medical ultrasonic imaging to solve the above problems. Objective: Under the condition of satisfying the speed of ultrasound imaging, the quality of imaging can be further improved to provide the basis for accurate medical diagnosis. Methods: According to CS theory and the characteristics of the array ultrasonic imaging system, block compressed sensing ultrasonic imaging algorithm is proposed based on wavelet sparse representation. Results: Three kinds of observation matrices have been designed on the basis of the proposed algorithm, which can be selected to reduce the number of the linear array channels and the complexity of the ultrasonic imaging system to some extent. Conclusion: The corresponding simulation program is designed, and the result shows that this algorithm can greatly reduce the total data amount required by imaging and the number of data channels required for linear array transducer to receive data. The imaging effect has been greatly improved compared with that of the spatial frequency domain sparse algorithm.

Mapping of fatty acids composition in shelled almonds analysed in bulk using a Hyperspectral Imaging system

LWT ◽  
2021 ◽  
Vol 138 ◽  
pp. 110678
Author(s):  
Irina Torres ◽  
Dolores Pérez-Marín ◽  
Miguel Vega-Castellote ◽  
María-Teresa Sánchez
Keyword(s):  
Fatty Acids ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Fatty Acids Composition

scholarly journals Correction for the Partial Volume Effects (PVE) in Nuclear Medicine Imaging: A Post-Reconstruction Analytic Method

2021 ◽  
Vol 11 (14) ◽  
pp. 6460
Author(s):  
Fabio Di Martino ◽  
Patrizio Barca ◽  
Eleonora Bortoli ◽  
Alessia Giuliano ◽  
Duccio Volterrani
Keyword(s):  
Nuclear Medicine ◽  
Imaging System ◽  
Tomographic Reconstruction ◽  
Mathematical Expression ◽  
Reconstruction Algorithm ◽  
Volume Effect ◽  
Nuclear Medicine Imaging ◽  
Theoretical Derivation ◽  
Reconstruction Process ◽  
Partial Volume

Quantitative analyses in nuclear medicine are increasingly used, both for diagnostic and therapeutic purposes. The Partial Volume Effect (PVE) is the most important factor of loss of quantification in Nuclear Medicine, especially for evaluation in Region of Interest (ROI) smaller than the Full Width at Half Maximum (FWHM) of the PSF. The aim of this work is to present a new approach for the correction of PVE, using a post-reconstruction process starting from a mathematical expression, which only requires the knowledge of the FWHM of the final PSF of the imaging system used. After the presentation of the theoretical derivation, the experimental evaluation of this method is performed using a PET/CT hybrid system and acquiring the IEC NEMA phantom with six spherical “hot” ROIs (with diameters of 10, 13, 17, 22, 28, and 37 mm) and a homogeneous “colder” background. In order to evaluate the recovery of quantitative data, the effect of statistical noise (different acquisition times), tomographic reconstruction algorithm with and without time-of-flight (TOF) and different signal-to-background activity concentration ratio (3:1 and 10:1) was studied. The application of the corrective method allows recovering the loss of quantification due to PVE for all sizes of spheres acquired, with a final accuracy less than 17%, for lesion dimensions larger than two FWHM and for acquisition times equal to or greater than two minutes.

scholarly journals Nondestructive and rapid determination of lignocellulose components of biofuel pellet using online hyperspectral imaging system

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Xuping Feng ◽  
Chenliang Yu ◽  
Xiaodan Liu ◽  
Yunfeng Chen ◽  
Hong Zhen ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System ◽  
Rapid Determination

Design of a Cooperative Chemical Agent (CA) Detection Algorithm with the Hyperspectral Imaging System

2021 ◽  
Author(s):  
Hyeong-Geun Yu ◽  
Whimin Kim ◽  
Dong-Jo Park ◽  
Dong Eui Chang ◽  
Hyunwoo Nam
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging System ◽  
Detection Algorithm ◽  
Chemical Agent

scholarly journals Prediction of Douglas-Fir Lumber Properties: Comparison between a Benchtop Near-Infrared Spectrometer and Hyperspectral Imaging System

2018 ◽  
Vol 8 (12) ◽  
pp. 2602 ◽  
Author(s):  
Laurence Schimleck ◽  
Joseph Dahlen ◽  
Seung-Chul Yoon ◽  
Kurt Lawrence ◽  
Paul Jones
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Nir Spectroscopy ◽  
Physical And Mechanical Properties ◽  
Douglas Fir ◽  
Modulus Of Rupture ◽  
Visual Grade ◽  
Fit Statistics ◽  
Infrared Spectrometer

Near-infrared (NIR) spectroscopy and NIR hyperspectral imaging (NIR-HSI) were compared for the rapid estimation of physical and mechanical properties of No. 2 visual grade 2 × 4 (38.1 mm by 88.9 mm) Douglas-fir structural lumber. In total, 390 lumber samples were acquired from four mills in North America and destructively tested through bending. From each piece of lumber, a 25-mm length block was cut to collect diffuse reflectance NIR spectra and hyperspectral images. Calibrations for the specific gravity (SG) of both the lumber (SGlumber) and 25-mm block (SGblock) and the lumber modulus of elasticity (MOE) and modulus of rupture (MOR) were created using partial least squares (PLS) regression and their performance checked with a prediction set. The strongest calibrations were based on NIR spectra; however, the NIR-HSI data provided stronger predictions for all properties. In terms of fit statistics, SGblock gave the best results, followed by SGlumber, MOE, and MOR. The NIR-HSI SGlumber, MOE, and MOR calibrations were used to predict these properties for each pixel across the transverse surface of the scanned samples, allowing SG, MOE, and MOR variation within and among rings to be observed.

Pork Quality Classification Using a Hyperspectral Imaging System and Neural Network

2007 ◽  
Vol 3 (1) ◽  
Author(s):  
Qiao Jun ◽  
Michael Ngadi ◽  
Ning Wang ◽  
Aynur Gunenc ◽  
Mariana Monroy ◽  
...  
Keyword(s):  
Neural Network ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Objective Assessment ◽  
Principal Component ◽  
Pork Quality ◽  
Pca Analysis ◽  
Error Frequency ◽  
Quality Classification ◽  
Average Reflectance

Pork quality is usually determined subjectively as PSE, PFN, RFN, RSE and DFD based on color, texture and exudation of the meat. In this study, a hyperspectral-imaging-based technique was developed to achieve rapid, accurate and objective assessment of pork quality. The principal component analysis (PCA) and stepwise operation methods were used to select feature waveband from the entire spectral wavelengths (430 to 980 nm). Then the feature waveband images were extracted at the selected feature wavebands from raw hyperspectral images, and the average reflectance (R) was calculated within the whole loin-eye area. Artificial neural network was used to classify these groups. Results showed that PCA analysis had a better performance than that of stepwise operation for feature waveband images selection. The 1st derivative data gave a better result than that of mean reflectance spectra data. The best classified result was 87.5% correction. The error frequency showed that RSE samples were easier to classify. The PFN and PSE samples were difficult to separate from each other.

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