High performance image mapping spectrometer (IMS) for snapshot hyperspectral imaging applications

Pawłowski, M.; Dwight, Jason G.; Nguyen, Thuc-Uyen; Tkaczyk, Tomasz S.

doi:10.1364/oe.27.001597

Cited by 34 publications

(12 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The snapshot method could simultaneously capture both spatial and spectral information, which enables real-time spectral imaging. As real-time imaging is very important in medical imaging areas, the snapshot method has been continuously developed to improve spatial and spectral resolution, while maintaining imaging speed [44][45][46][47].…”

Section: Medical Hyperspectral Imaging Systemmentioning

confidence: 99%

Hyperspectral Imaging for Clinical Applications

Yoon

2022

BioChip J

View full text Add to dashboard Cite

Measuring morphological and biochemical features of tissue is crucial for disease diagnosis and surgical guidance, providing clinically significant information related to pathophysiology. Hyperspectral imaging (HSI) techniques obtain both spatial and spectral features of tissue without labeling molecules such as fluorescent dyes, which provides rich information for improved disease diagnosis and treatment. Recent advances in HSI systems have demonstrated its potential for clinical applications, especially in disease diagnosis and image-guided surgery. This review summarizes the basic principle of HSI and optical systems, deep-learning-based image analysis, and clinical applications of HSI to provide insight into this rapidly growing field of research. In addition, the challenges facing the clinical implementation of HSI techniques are discussed.

show abstract

Section: Medical Hyperspectral Imaging Systemmentioning

confidence: 99%

Hyperspectral Imaging for Clinical Applications

Yoon

2022

BioChip J

View full text Add to dashboard Cite

show abstract

“…The data captured by the imaging spectrometer is referred to as a three-dimensional data cube denoted by two-dimensional space and one-dimensional spectrum [ 31 ]. The data cube contains spectral and spatial information of the targets within the whole scanning range of the imaging spectrometer, as shown in Figure 1 c. Each target in the scanning range corresponds to a column in the data cube which can be converted into a spectral curve for spectrum characteristics analysis, as shown in Figure 1 d,e.…”

Section: Imaging Principlementioning

confidence: 99%

Optical Design of Imaging Spectrometer Based on Linear Variable Filter for Nighttime Light Remote Sensing

Xie

Liu

et al. 2021

Sensors

View full text Add to dashboard Cite

Nighttime light remote sensing has unique advantages on reflecting human activities, and thus has been used in many fields including estimating population and GDP, analyzing light pollution and monitoring disasters and conflict. However, the existing nighttime light remote sensors have many limitations because they are subject to one or more shortcomings such as coarse spatial resolution, restricted swath width and lack of multi-spectral data. Therefore, we propose an optical system of imaging spectrometer based on linear variable filter. The imaging principle, optical specifications, optical design, imaging performance analysis and tolerance analysis are illustrated. The optical system with a focal length of 100 mm, F-number 4 and 43° field of view in the spectrum range of 400–1000 nm is presented, and excellent image quality is achieved. The system can obtain the multi-spectral images of eight bands with a spatial resolution of 21.5 m and a swath width of 320 km at the altitude of 500 km. Compared with the existing nighttime light remote sensors, our system possesses the advantages of high spatial and high spectral resolution, wide spectrum band and wide swath width simultaneously, greatly making up for the shortage of the present systems. The result of tolerance analysis shows our system satisfy the requirements of fabrication and alignment.

show abstract

“…To measure the spectral characteristics of each spatial point in real time, the snapshot spectral imaging techniques emerges in recent years including direct measurement strategy and computational imaging strategy. The former one includes the approaches of image-division [13], aperture-division [14] and optical-path-division [15] formations, and the latter one includes the approaches based on computed tomography [16], compressed sensing (CS) [17] and Fourier transform [18].…”

Section: Introductionmentioning

confidence: 99%

Snapshot depth–spectral imaging based on image mapping and light field

Ding

Zhou

et al. 2023

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Depth–spectral imaging (DSI) is an emerging technology which can obtain and reconstruct the spatial, spectral and depth information of a scene simultaneously. Conventionally, DSI system usually relies on scanning process, multi-sensors or compressed sensing framework to modulate and acquire the entire information. This paper proposes a novel snapshot DSI architecture based on image mapping and light field framework by using a single format detector. Specifically, we acquire the depth – spectral information in two steps. Firstly, an image mapper is utilized to slice and reflect the first image to different directions which is a spatial modulation processing. The modulated light wave is then dispersed by a direct vision prism. After re-collection, the sliced dispersed light wave is recorded by a light field sensor. Complimentary, we also propose a reconstruction strategy to recover the spatial depth – spectral hypercube effectively. We establish a mathematical model to describe the light wave distribution on every optical facet. Through simulations, we generate the aliasing raw spectral light field data. Under the reconstruction strategy, we design an algorithm to recover the hypercube accurately. Also, we make an analysis about the spatial and spectral resolution of the reconstructed data, the evaluation results conform the expectation.

show abstract

High performance image mapping spectrometer (IMS) for snapshot hyperspectral imaging applications

Cited by 34 publications

References 31 publications

Hyperspectral Imaging for Clinical Applications

Hyperspectral Imaging for Clinical Applications

Optical Design of Imaging Spectrometer Based on Linear Variable Filter for Nighttime Light Remote Sensing

Snapshot depth–spectral imaging based on image mapping and light field

Contact Info

Product

Resources

About