Deep learning approach for hyperspectral image demosaicking, spectral correction and high-resolution RGB reconstruction Article Swipe
Peichao Li
,
Michael Ebner
,
Philip J. Noonan
,
Conor C. Horgan
,
Anisha Bahl
,
Sébastien Ourselin
,
Jonathan Shapey
,
Tom Vercauteren
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1080/21681163.2021.1997646
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1080/21681163.2021.1997646
Hyperspectral imaging is one of the most promising techniques for intraoperative tissue characterisation. Snapshot mosaic cameras, which can capture hyperspectral data in a single exposure, have the potential to make a real-time hyperspectral imaging system for surgical decision-making possible. However, optimal exploitation of the captured data requires solving an ill-posed demosaicking problem and applying additional spectral corrections to recover spatial and spectral information of the image. In this work, we propose a deep learning-based image demosaicking algorithm for snapshot hyperspectral images using supervised learning methods. Due to the lack of publicly available medical images acquired with snapshot mosaic cameras, a synthetic image generation approach is proposed to simulate snapshot images from existing medical image datasets captured by high-resolution, but slow, hyperspectral imaging devices. Image reconstruction is achieved using convolutional neural networks for hyperspectral image super-resolution, followed by cross-talk and leakage correction using a sensor-specific calibration matrix. The resulting demosaicked images are evaluated both quantitatively and qualitatively, showing clear improvements in image quality compared to a baseline demosaicking method using linear interpolation. Moreover, the fast processing time of~45\,ms of our algorithm to obtain super-resolved RGB or oxygenation saturation maps per image frame for a state-of-the-art snapshot mosaic camera demonstrates the potential for its seamless integration into real-time surgical hyperspectral imaging applications.
Related Topics
Concepts
Hyperspectral imaging
Artificial intelligence
Demosaicing
Snapshot (computer storage)
Computer vision
Computer science
RGB color model
Spectral imaging
Image resolution
Full spectral imaging
Convolutional neural network
Pattern recognition (psychology)
Image processing
Remote sensing
Color image
Image (mathematics)
Geography
Operating system
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1080/21681163.2021.1997646
- OA Status
- hybrid
- References
- 21
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3197430628
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3197430628Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/21681163.2021.1997646Digital Object Identifier
- Title
-
Deep learning approach for hyperspectral image demosaicking, spectral correction and high-resolution RGB reconstructionWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-11-30Full publication date if available
- Authors
-
Peichao Li, Michael Ebner, Philip J. Noonan, Conor C. Horgan, Anisha Bahl, Sébastien Ourselin, Jonathan Shapey, Tom VercauterenList of authors in order
- Landing page
-
https://doi.org/10.1080/21681163.2021.1997646Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1080/21681163.2021.1997646Direct OA link when available
- Concepts
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Hyperspectral imaging, Artificial intelligence, Demosaicing, Snapshot (computer storage), Computer vision, Computer science, RGB color model, Spectral imaging, Image resolution, Full spectral imaging, Convolutional neural network, Pattern recognition (psychology), Image processing, Remote sensing, Color image, Image (mathematics), Geography, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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21Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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