A heuristic technique for CTIS image reconstruction

Vose, Michael D.; Horton, Mitchel

doi:10.1364/ao.46.006498

Cited by 12 publications

(10 citation statements)

References 16 publications

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“…Then complex algorithms, related to computed tomography (CT)-reconstruction algorithms, are used to extract a spectral cube from this overlapping projected data. Drawbacks of this approach include the complexity of the reconstruction algorithms rendering real-time visualization impossible and the limited resolution of the spectral cube, with pixel counts of unrolled cubes varying from 4.2% of the size of the sensor [9] to 22-64% of the size of the sensor [10]. Moreover, its main limitation as a snapshot instrument is due to the nature of its ill-posed process of acquisition of limited angle projection tomography.…”

Section: State Of the Artmentioning

confidence: 99%

A snapshot multispectral imager with integrated tiled filters and optical duplication

2013

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Although the potential of spectral imaging has been demonstrated in research environments, its adoption by industry has so far been limited due to the lack of high speed, low cost and compact spectral cameras. We have previously presented work to overcome this limitation by monolithically integrating optical interference filters on top of standard CMOS image sensors for high resolution pushbroom hyperspectral cameras. These cameras require a scanning of the scene and therefore introduce operator complexity due to the need for synchronization and alignment of the scanning to the camera. This typically leads to problems with motion blur, reduced SNR in high speed applications and detection latency and overall restricts the types of applications that can use this system. This paper introduces a novel snapshot multispectral imager concept based on optical filters monolithically integrated on top of a standard CMOS image sensor. By using monolithic integration for the dedicated, high quality spectral filters at its core, it enables the use of mass-produced fore-optics, reducing the total system cost. It overcomes the problems mentioned for scanning applications by snapshot acquisition, where an entire multispectral data cube is sensed at one discrete point in time. This is achieved by applying a novel, tiled filter layout and an optical sub-system which simultaneously duplicates the scene onto each filter tile. Through the use of monolithically integrated optical filters it retains the qualities of compactness, low cost and high acquisition speed, differentiating it from other snapshot spectral cameras based on heterogeneously integrated custom optics. Moreover, thanks to a simple cube assembly process, it enables real-time, low-latency operation. Our prototype camera can acquire multispectral image cubes of 256x256 pixels over 32 bands in the spectral range of 600-1000nm at a speed of about 30 cubes per second at daylight conditions up to 340 cubes per second at higher illumination levels as typically used in machine vision applications.

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Section: State Of the Artmentioning

confidence: 99%

A snapshot multispectral imager with integrated tiled filters and optical duplication

2013

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“…1c, respectively, and a true RGB image of the ColorChecker is shown in As CTIS images are compressed and not as easy to analyze as hyperspectral cubes, fast and precise real-time reconstruction of the hyperspectral cube from a CTIS image is an important but challenging goal. In practice, the common dimension of the hyperspectral cube often exceeds 100 × 100 × 100, resulting in long reconstruction times and mediocre accuracy for existing algorithms [14][15][16]. Therefore, we consider neural networks to circumvent the limitations of current reconstruction algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…15) pixels between the nearby hyperspectral cubes along the vertical (horizontal) direction on 2-D images, i.e., strides =(10,15) for the cropping window. By doing so, the network is beneficially allowed to see many more different combinations of original hyperspectral cubes without incurring an enormous dataset.…”

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confidence: 99%

“…A CTIS image contains a central zeroth-order undiffracted image surrounded by four first-order diffracted images. Different algorithms[14][15][16] are used to convert multiplexed 2-D CTIS images into 3-D hyperspectral cubes, which are easier to visualize and analyze. A CTIS camera is, however, portable and cheaper than hyperspectral imagers such as pushbroom and has a wider range of applications.…”

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confidence: 99%

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The Application of Convolutional Neural Networks for Tomographic Reconstruction of Hyperspectral Images

Huang¹,

Peters²,

Ahlebaek³

et al. 2021

Preprint

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A novel method, utilizing convolutional neural networks (CNNs), is proposed to reconstruct hyperspectral cubes from computed tomography imaging spectrometer (CTIS) images. Current reconstruction algorithms are usually subject to long reconstruction times and mediocre precision in cases of a large number of spectral channels. The constructed CNNs deliver higher precision and shorter reconstruction time than a standard expectation maximization algorithm. In addition, the network can handle two different types of real-world images at the same time -specifically ColorChecker and carrot spectral images are considered. This work paves the way toward real-time reconstruction of hyperspectral cubes from CTIS images.

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“…18,22 Since potential applications for CTIS are embedded realtime or near-realtime systems, a better reconstruction algorithm is needed. Previous attempts at speeding up the reconstruction algorithm have been performed by Hagen 18 and Vose-Horton 1,23 where they developed fast reconstruction algorithms exploiting spatial shift-invariance (hereafter referred to as shift-invariance). Additionally, hardware accelerations were performed by Thompson 22 and Sethaphong 24 using cell processors and supercomputers.…”

Section: Introductionmentioning

confidence: 99%

Accelerating computed tomographic imaging spectrometer reconstruction using a parallel algorithm exploiting spatial shift-invariance

2020

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Computed Tomographic Imaging Spectrometers (CTIS) capture hyperspectral images in realtime. However, post processing the imagery can require enormous computational resources; thus, limiting its application to nonrealtime scenarios. To overcome these challenges we developed a highly parallelizable algorithm that exploits spatial shift-invariance. To demonstrate the versatility of our new algorithm, we developed implementations on a desktop and an embedded graphics processing unit (GPU). To our knowledge, our results show the fastest image reconstruction times reported.

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A heuristic technique for CTIS image reconstruction

Cited by 12 publications

References 16 publications

A snapshot multispectral imager with integrated tiled filters and optical duplication

A snapshot multispectral imager with integrated tiled filters and optical duplication

The Application of Convolutional Neural Networks for Tomographic Reconstruction of Hyperspectral Images

Accelerating computed tomographic imaging spectrometer reconstruction using a parallel algorithm exploiting spatial shift-invariance

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