scholarly journals Spectral Imaging Method for Transmissive Media

2021 ◽  
Vol 2021 (1) ◽  
pp. 51-55
Author(s):  
David R. Wyble
Keyword(s):  
Spectral Imaging ◽  
Measured Data ◽  
Spectral Model ◽  
Imaging Method ◽  
Spectral Transmittance ◽  
Spectral Image ◽  
Large Format ◽  
Colorimetric Data ◽  
Led Backlight ◽  
Back Illuminated

An imaging process is described which captures spectral transmittance for transmissive media. The specific application is positive and negative large-format film. The system is based on a ten channel LED backlight source and a monochrome camera. The LED source sequentially back-illuminated reference targets and film samples, with an image captured for each LED channel. From the measured data and images of reference targets, a model was developed to predict spectral transmittance. With that model, the 10 images of a sample were combined to a single 31-band spectral image. Spectral images can be used to calculate colorimetric data for each pixel. These colorimetric results show that the system produces good colorimetric predictions when compared to the most relevant FADGI guidelines. Some improvement is required for the spectral model particularly in the red region.

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.

Multispectral Bayesian reconstruction technique for real-time two color fluorescence microscopy

RSC Advances ◽  
2015 ◽  
Vol 5 (17) ◽  
pp. 13175-13183 ◽  
Author(s):  
Shilpa Dilipkumar ◽  
Ravi Manjithaya ◽  
Partha Pratim Mondal
Keyword(s):  
Image Reconstruction ◽  
Fluorescence Microscopy ◽  
Real Time ◽  
Spectral Imaging ◽  
Reconstruction Technique ◽  
Imaging Method ◽  
Combined Approach ◽  
Bayesian Reconstruction ◽  
Image Reconstruction Technique ◽  
Bayesian Image Reconstruction

We have developed a real-time imaging method for two-color widefield fluorescence microscopy using a combined approach that integrates multi-spectral imaging and Bayesian image reconstruction technique.

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>

scholarly journals Novel Spectral Imaging Method for Fizeau Interferometers

2008 ◽  
Vol 60 (2) ◽  
pp. 303-314
Author(s):  
Taro Matsuo ◽  
Hiroshi Shibai ◽  
Mitsunobu Kawada ◽  
Makoto Hattori ◽  
Izumi S. Ohta ◽  
...  
Keyword(s):  
Spectral Imaging ◽  
Imaging Method

scholarly journals QUBIC: Exploring the Primordial Universe with the Q&U Bolometric Interferometer

Universe ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 42 ◽  
Author(s):  
Aniello Mennella ◽  
Peter Ade ◽  
Giorgio Amico ◽  
Didier Auguste ◽  
Jonathan Aumont ◽  
...  
Keyword(s):  
Frequency Band ◽  
State Of The Art ◽  
Spectral Imaging ◽  
Measured Data ◽  
Instrumental Effects ◽  
Main Frequency ◽  
Bolometric Detectors ◽  
Novel Approach ◽  
Systematic Effects ◽  
Final Instrument

In this paper, we describe QUBIC, an experiment that will observe the polarized microwave sky with a novel approach, which combines the sensitivity of state-of-the-art bolometric detectors with the systematic effects control typical of interferometers. QUBIC’s unique features are the so-called “self-calibration”, a technique that allows us to clean the measured data from instrumental effects, and its spectral imaging power, i.e., the ability to separate the signal into various sub-bands within each frequency band. QUBIC will observe the sky in two main frequency bands: 150 GHz and 220 GHz. A technological demonstrator is currently under testing and will be deployed in Argentina during 2019, while the final instrument is expected to be installed during 2020.

Using quantitative diffuse multi spectral imaging method to assess Cushing disease treatment outcome

2014 ◽  
Author(s):  
A. Afshari ◽  
MB Lodish ◽  
Y. Ardeshirpour ◽  
E. Gourgari ◽  
M. Keil ◽  
...  
Keyword(s):  
Treatment Outcome ◽  
Spectral Imaging ◽  
Imaging Method ◽  
Disease Treatment ◽  
Cushing Disease
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