Graph and Rank Regularized Matrix Recovery for Snapshot Spectral Image Demosaicing

Tsagkatakis, Grigorios; Bloemen, Maarten; Geelen, Bert; Jayapala, Murali; Tsakalides, Panagiotis

doi:10.1109/tci.2018.2888989

Cited by 31 publications

(15 citation statements)

References 49 publications

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“…The TRevSCI neural network and MSFA with real filters are used to reconstruct the datacube. The real filters use the spectral response of the IMEC camera with 16 and 25 filters [6], [7]. Table II shows the corresponding results, where we note the superior performance of the MSFA-OSP design for both numbers of filters.…”

Section: G Results Using Real Filtersmentioning

confidence: 99%

“…In particular, the MSFAs have a spatial resolution of 256 × 256 and the number of spectral filters are L = 16 or L = 25. In our evaluation, we use ideal dichroic filters and also real filters with the spectral response taken from the IMEC [6], [7] camera, which is a unique off-the-self MSFA-based system available on the market. For additional information about the simulation experiments with real filters, see subsection IV-G.…”

Section: B Comparison Between State-of-the-art Msfasmentioning

confidence: 99%

“…The optimal design maximizes the minimum distance between spectral filters of the MSFA. This framework provides an explanation of why the-state-of-the-art approaches such as Blue Noise (BN) Coded Aperture (CA) [10], MSFA-IMEC with 16 and 25 filters [6], [7], and MSFA-BTES with 16 filters [5], are non-optimal (see Table I, and II; Fig. 4 and Fig.…”

Section: Introductionmentioning

confidence: 99%

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Multispectral Filter Array Design by Optimal Sphere Packing

Díaz¹,

Vidal²,

Meza³

et al. 2022

Preprint

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<p>Multispectral Imaging (MSI) collects a datacube of spatio-spectral information of a scene. Many acquisition methods for spectral imaging use scanning, preventing its widespread usage for dynamic scenes. On the other hand, the conventional color filter array (CFA) method often used to sample color images has also been extended to snapshot MSI using a Multispectral Filter Array (MSFA), which is a mosaic of selective spectral filters placed over the Focal Plane Array (FPA). However, even state-of-the-art MSFAs coding patterns produce artifacts and distortions in the reconstructed spectral images, which might be due to the non-optimal distribution of the spectral filters. To reduce the appearance of artifacts and provide tools for the optimal design of MSFAs, this paper proposes a novel mathematical framework to design MSFAs using a Sphere Packing (SP) approach. By assuming that each sampled filter can be represented by a sphere within the discrete datacube, SP organizes the position of the equal-size and disjoint spheres’s centers in a cubic container. Our method is denoted Multispectral Filter Array by Optimal Sphere Packing (MSFA-OSP), which seeks filter positions that maximize the minimum distance between the spheres’s centers. Simulation results show an image quality improvement of up to 2 dB and a remarkable boost in spectral similarity when using our proposed MSFA design approach for a variety of reconstruction algorithms. Moreover, MSFA-OSP notably reduces the appearance of false colors and zipper effect artifacts, often seen when using state-of-the-art demosaicking algorithms. Experiments using synthetic and real data prove that the proposed MSFA-OSP outperforms state-of-the-art MSFAs in terms of spatial and spectral fidelity.</p>

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Section: G Results Using Real Filtersmentioning

confidence: 99%

Section: B Comparison Between State-of-the-art Msfasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multispectral Filter Array Design by Optimal Sphere Packing

Díaz¹,

Vidal²,

Meza³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For increased real-time imaging speed, recently developed snapshot HSI camera systems have been used to assess brain perfusion in neurosurgery (Pichette et al 2016 ) and to perform preclinical skin perfusion analysis (Ewerlöf et al 2017 ). However, while snapshot HSI sensors permit real-time HSI capture with video-rate imaging, spatial resolution is limited and needs to be accounted for in a post-processing step called demosaicking (Dijkstra et al 2019 , Tsagkatakis et al 2019 ). Moreover, previously presented snapshot iHSI works did not methodologically map out and address the critical design considerations to ensure a seamless integration into the surgical workflow.…”

Section: Introductionmentioning

confidence: 99%

Intraoperative hyperspectral label-free imaging: from system design to first-in-patient translation

Ebner

Nabavi

Shapey

et al. 2021

J. Phys. D: Appl. Phys.

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Despite advances in intraoperative surgical imaging, reliable discrimination of critical tissue during surgery remains challenging. As a result, decisions with potentially life-changing consequences for patients are still based on the surgeon’s subjective visual assessment. Hyperspectral imaging (HSI) provides a promising solution for objective intraoperative tissue characterisation, with the advantages of being non-contact, non-ionising and non-invasive. However, while its potential to aid surgical decision-making has been investigated for a range of applications, to date no real-time intraoperative HSI (iHSI) system has been presented that follows critical design considerations to ensure a satisfactory integration into the surgical workflow. By establishing functional and technical requirements of an intraoperative system for surgery, we present an iHSI system design that allows for real-time wide-field HSI and responsive surgical guidance in a highly constrained operating theatre. Two systems exploiting state-of-the-art industrial HSI cameras, respectively using linescan and snapshot imaging technology, were designed and investigated by performing assessments against established design criteria and ex vivo tissue experiments. Finally, we report the use of our real-time iHSI system in a clinical feasibility case study as part of a spinal fusion surgery. Our results demonstrate seamless integration into existing surgical workflows.

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“…Classification includes signal classification [1,2], image classification [3][4][5], mail classification [6,7] and so on [8][9][10][11]. The essence of classification is to determine the categories of data.…”

Section: Introductionmentioning

confidence: 99%

A Novel Broad Learning Model-Based Semi-Supervised Image Classification Method

2020

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Broad learning system (BLS) is an effective and efficient incremental learning system without the deep architecture. It has strong feature extraction ability and high computational efficiency. However, it is greatly limited in the applicability of supervised learning. For the collected actual data, more data are unlabeled data and less data are labeled data. To overcome these problems, Fick's law assisted propagation (FLAP) is introduced into the BLS to propose a new semi-supervised classification algorithm, namely FLAP-BLS in this paper. In the FLAP-BLS, the FLAP has the labeled ability from the labeled examples to unlabeled examples, it is used to mark plenty of unlabeled samples by few labeled samples in order to obtain a large number of labeled samples and build the sample data matrix. Then an efficient incremental BLS without deep structure can effectively extract features from large-scale data, it is used to effectively classify the sample matrix. Finally, USPS, MNIST and NORB datasets are selected to validate the effectiveness of the FLAP-BLS. The experiment results show that the FLAP-BLS can effectively classify the few labeled samples and a large of unlabeled samples and obtain classification results with high accuracy, and it has faster classification speed, stronger generalization ability and better stability. The proposed method provides a new method for image classification.

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Graph and Rank Regularized Matrix Recovery for Snapshot Spectral Image Demosaicing

Cited by 31 publications

References 49 publications

Multispectral Filter Array Design by Optimal Sphere Packing

Multispectral Filter Array Design by Optimal Sphere Packing

Intraoperative hyperspectral label-free imaging: from system design to first-in-patient translation

A Novel Broad Learning Model-Based Semi-Supervised Image Classification Method

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