scholarly journals Spectral Imaging Experiments with Various Optical Schemes Based on the Same AOTF

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2984
Author(s):  
Vladislav Batshev ◽  
Alexander Machikhin ◽  
Alexey Gorevoy ◽  
Grigoriy Martynov ◽  
Demid Khokhlov ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Image Quality ◽  
Optical System ◽  
Spectral Imaging ◽  
Cost Effective ◽  
Tunable Filters ◽  
Spectral Image ◽  
Image Filtration ◽  
Spectral Imager ◽  
Coupling Components

Spectral image filtration by means of acousto-optical tunable filters (AOTFs) has multiple applications. For its implementation, a few different optical schemes are in use. They differ in image quality, number of coupling components, dimensions and alignment complexity. To choose the optical system of AOTF-based spectral imager properly, many factors have to be considered. Though various schemes of acousto-optic (AO) filtration have been tested and discussed, their comparative analysis has not been reported up to now. In this study, we assembled the four most popular schemes (confocal, collimating, tandem and double-path) using the same AO cells and experimentally compared their main features. Depending on the application, each scheme may be the basis of compact cost-effective spectral imaging devices.

scholarly journals Recovering spectral images from compressive measurements using designed coded apertures and Matrix Completion Theory

2017 ◽  
Vol 21 (2) ◽  
Author(s):  
Tatiana Gelvez ◽  
Hoover Rueda ◽  
Henry Arguello
Keyword(s):  
Imaging Techniques ◽  
Matrix Completion ◽  
Spectral Imaging ◽  
Spatial Location ◽  
Low Rank ◽  
Coded Aperture ◽  
Reconstruction Algorithms ◽  
Spectral Image ◽  
The Matrix ◽  
Spectral Imager

<p>Spectral imaging aims to capture and process a 3-dimensional spectral image with a large amount of spectral information for each spatial location. Compressive spectral imaging techniques (CSI) increases the sensing speed and reduces the amount of collected data compared to traditional spectral imaging methods. The coded aperture snapshot spectral imager (CASSI) is an optical architecture to sense a spectral image in a single 2D coded projection by applying CSI. Typically, the 3D scene is recovered by solving an L1-based optimization problem that assumes the scene is sparse in some known orthonormal basis. In contrast, the matrix completion technique (MC) allows to recover the scene without such prior knowledge. The MC reconstruction algorithms rely on a low-rank structure of the scene. Moreover, the CASSI system uses coded aperture patterns that determine the quality of the estimated scene. Therefore, this paper proposes the design of an optimal coded aperture set for the MC methodology. The designed set is attained by maximizing the distance between the translucent elements in the coded aperture. Visualization of the recovered spectral signals and simulations over different databases show average improvement when the designed coded set is used between 1-3 dBs compared to the complementary coded aperture set, and between 3-9 dBs compared to the conventional random coded aperture set.</p>

Compact AOTF-based spectral imaging system for medical endoscopic analysis

2013 ◽  
Vol 2 (2) ◽  
Author(s):  
Alexander Machihin ◽  
Vitold Pozhar ◽  
Vladislav Batshev
Keyword(s):  
Spectral Analysis ◽  
Image Registration ◽  
Real Time ◽  
Imaging System ◽  
Spectral Imaging ◽  
Tunable Filters ◽  
Image Distortion ◽  
Spectral Features ◽  
Spectral Image ◽  
Reliable Detection

AbstractA prototype of a spectral imaging module is described which can be attached to conventional rigid and flexible medical endoscopes. It is based on acousto-optic tunable filters (AOTF) and provides fast spectral image registration at an arbitrary series of wavelengths. The main advantage of the device is the minimization of spatial and spectral image distortion by use of a specialized double AOTF monochromator. These properties ensure immediate and reliable detection of spectral features in any image pixel. Real-time spectral analysis, in addition to spectral visualization, provides the opportunity to make medical photoluminescence diagnostics more effective.

scholarly journals A comparative analysis of non­reference assessment methods

Connectivity ◽  
2020 ◽  
Vol 148 (6) ◽  
Author(s):  
V. V. Grebenyuk ◽  
◽  
O. A. Dibrivnyy ◽  
O. V. Nehodenko
Keyword(s):  
Comparative Analysis ◽  
Image Quality ◽  
Image Quality Assessment ◽  
Excellent Result ◽  
Video Quality ◽  
Mean Value ◽  
Performance Comparison ◽  
Quality Level ◽  
Reference Methods

A comparative analysis of functions to assess image quality in the absence of a sample: no-reference (NR) measure or NR-type methods. The availability of NR-methods is very important for assessing the quality of streaming video such as television, game streaming, online conferences, web-chatting, etc. (because on the side of the recipient of the video there is no standard for quality comparison) and assessing the results of transformations aimed at improving video, and choosing the parameters of these transformations (brightness change, semitone and others). The human visual system (HVS) is able to visually assessing video quality, but If required to visually assess the quality of dozens or hundreds of videos or ranking them by quality level it will be needed a huge amount of time. Six types of experiments were performed to analyze the correlation of calculated quantitative estimates with visual assessments of the quality of the tested video files. Three of them are fundamentally new: comparing video after gamma correction and changing the contrast with different parameters, as well as blurring, which may be the result of defocusing the camcorder. A hybrid method (or reduced-reference (RR) measure) and a full-reference (FR) measure or FR-type method were also added for comparison. It has been experimentally shown that none of the studied non-reference methods of image quality assessment is universal, and the calculated assessment cannot be converted into a quality scale without taking into account the factors influencing the distortion of image quality. Moreover, all NR-type methods could not cope with the experiment of changing the contrast, believing that the best result is the most contrasting image but the original. Instead, the reference methods showed an excellent result (except one, which showed partial ineffectiveness). Also, it has been shown performance comparison between methods. It is shown that most of the studied methods calculate local estimates for each frame, and their arithmetic mean value is an estimate of the quality of the entire video file. If the video is dominated by large areas of uniform evaluation, methods of this type may give incorrect quality evaluations that do not coincide with the visual evaluations.

scholarly journals Block-Based Spectral Image Reconstruction For Compressive Spectral Imaging Using Smoothness on Graphs

2021 ◽  
Author(s):  
Juan Florez Ospina ◽  
Abdullah Alrushud ◽  
Daniel Lau ◽  
Gonzalo Arce
Keyword(s):  
Image Reconstruction ◽  
Spectral Imaging ◽  
Spectral Image ◽  
Block Based

scholarly journals Deep learning classifiers for near infrared spectral imaging: a tutorial

2020 ◽  
Author(s):  
Jun-Li Xu ◽  
Cecilia Riccioli ◽  
Ana Herrero-Langreo ◽  
Aoife Gowen
Keyword(s):  
Deep Learning ◽  
Visual Recognition ◽  
Near Infrared ◽  
Three Dimensional ◽  
Spectral Imaging ◽  
Learning Approaches ◽  
Imaging Data ◽  
Spectral Image ◽  
Background Removal ◽  
Wide Range

Deep learning (DL) has recently achieved considerable successes in a wide range of applications, such as speech recognition, machine translation and visual recognition. This tutorial provides guidelines and useful strategies to apply DL techniques to address pixel-wise classification of spectral images. A one-dimensional convolutional neural network (1-D CNN) is used to extract features from the spectral domain, which are subsequently used for classification. In contrast to conventional classification methods for spectral images that examine primarily the spectral context, a three-dimensional (3-D) CNN is applied to simultaneously extract spatial and spectral features to enhance classificationaccuracy. This tutorial paper explains, in a stepwise manner, how to develop 1-D CNN and 3-D CNN models to discriminate spectral imaging data in a food authenticity context. The example image data provided consists of three varieties of puffed cereals imaged in the NIR range (943–1643 nm). The tutorial is presented in the MATLAB environment and scripts and dataset used are provided. Starting from spectral image pre-processing (background removal and spectral pre-treatment), the typical steps encountered in development of CNN models are presented. The example dataset provided demonstrates that deep learning approaches can increase classification accuracy compared to conventional approaches, increasing the accuracy of the model tested on an independent image from 92.33 % using partial least squares-discriminant analysis to 99.4 % using 3-CNN model at pixel level. The paper concludes with a discussion on the challenges and suggestions in the application of DL techniques for spectral image classification.

scholarly journals Brain Tumor Detection from MR Images using Image Process Techniques and Tools in Matlab Software

2021 ◽  
Vol 1 (4) ◽  
pp. 1-4
Author(s):  
Faisal Rehman ◽  
◽  
Syed Sheeraz Ali ◽  
Hamadullah Panhwar ◽  
Dr. Akhtar Hussain Phul ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Image Quality ◽  
Research Work ◽  
Cost Effective ◽  
Image Process ◽  
Medical Sciences ◽  
Mr Images ◽  
Research Areas ◽  
Tumor Identification ◽  
The Brain

In the medical era the Brain tumor is one of the most important research areas in the field of medical sciences. Researcher are trying to find the reliable and cost effective medical equipment’s for the cancer and its type for the diagnosed, especially tumor has deferent kinds but the major two type are discussed in this research paper. Which are the benign and Pre-Malignant, this research work is proposed for these factors such as the accuracy of the MRI image for the tumor identification and actual placing were taken into consideration. In this study, an algorithm is proposed to detect the brain tumor from magnetic resonance image (MRI) data simple. As enhance the image quality for the easiness the tumor treatments and diagnosed for the patients. The proposed algorithm enhances the MR image quality and detects the Brain tumor which helps the Physician to diagnose the tumor easily. As well this algorithm automatically calculates the area of tumor, size and location of the tumor where it is present for diagnostic the Patient.

Optical System of the Hyper-spectral Imager for the Underwater Environment and Targets Monitoring

2018 ◽  
Vol 47 (11) ◽  
pp. 1101003 ◽  
Author(s):  
于磊 YU Lei ◽  
徐明明 XU Ming-ming ◽  
陈结祥 CHEN Jie-xiang ◽  
薛辉 XUE Hui
Keyword(s):  
Optical System ◽  
Underwater Environment ◽  
Hyper Spectral ◽  
Spectral Imager

scholarly journals Video rate spectral imaging using a coded aperture snapshot spectral imager

2009 ◽  
Vol 17 (8) ◽  
pp. 6368 ◽  
Author(s):  
Ashwin A. Wagadarikar ◽  
Nikos P. Pitsianis ◽  
Xiaobai Sun ◽  
David J. Brady
Keyword(s):  
Spectral Imaging ◽  
Coded Aperture ◽  
Spectral Imager

scholarly journals Automated, Cost-Effective Optical System for Accelerated Antimicrobial Susceptibility Testing (AST) Using Deep Learning

ACS Photonics ◽  
2020 ◽  
Vol 7 (9) ◽  
pp. 2527-2538
Author(s):  
Calvin Brown ◽  
Derek Tseng ◽  
Paige M. K. Larkin ◽  
Susan Realegeno ◽  
Leanne Mortimer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical System ◽  
Antimicrobial Susceptibility ◽  
Susceptibility Testing ◽  
Cost Effective ◽  
Antimicrobial Susceptibility Testing
Sign in / Sign up

Export Citation Format

Share Document