Learnable Reconstruction Methods from RGB Images to Hyperspectral Imaging: A Survey

• URL: http://arxiv.org/abs/2106.15944v1
• Date: Wed, 30 Jun 2021 09:52:41 GMT
• Title: Learnable Reconstruction Methods from RGB Images to Hyperspectral Imaging: A Survey
• Authors: Jingang Zhang and Runmu Su and Wenqi Ren and Qiang Fu and Yunfeng Nie
• Abstract summary: Many alternative spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from lower-cost, more available RGB images. We present a thorough investigation of these state-of-the-art spectral reconstruction methods from the widespread RGB images. Most of the data-driven deep learning methods are superior to prior-based methods in terms of reconstruction accuracy and quality despite lower speeds.
• Score: 27.235897806207706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral imaging enables versatile applications due to its competence in capturing abundant spatial and spectral information, which are crucial for identifying substances. However, the devices for acquiring hyperspectral images are expensive and complicated. Therefore, many alternative spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from lower-cost, more available RGB images. We present a thorough investigation of these state-of-the-art spectral reconstruction methods from the widespread RGB images. A systematic study and comparison of more than 25 methods has revealed that most of the data-driven deep learning methods are superior to prior-based methods in terms of reconstruction accuracy and quality despite lower speeds. This comprehensive review can serve as a fruitful reference source for peer researchers, thus further inspiring future development directions in related domains.

Related papers

• A self-supervised and adversarial approach to hyperspectral demosaicking and RGB reconstruction in surgical imaging [3.426432165500852]
  Hyperspectral imaging holds promises in surgical imaging by offering biological tissue differentiation capabilities with detailed information that is invisible to the naked eye. For intra-operative guidance, real-time spectral data capture and display is mandated. Snapshot mosaic hyperspectral cameras are currently seen as the most suitable technology given this requirement. We present a self-supervised demosaicking and RGB reconstruction method that does not depend on paired high-resolution data as ground truth.
  arXiv  Detail & Related papers  (2024-07-27T15:29:35Z)
• Deep learning-based hyperspectral image reconstruction for quality assessment of agro-product [0.0]
  The goal of this study was to reconstruct hyperspectral images from RGB images through deep learning for agricultural applications. The algorithm accurately reconstructed the hyperspectral images from RGB images, with the resulting spectra closely matching the ground-truth. These findings highlight the potential of deep learning-based hyperspectral image reconstruction as a low-cost, efficient tool for various agricultural uses.
  arXiv  Detail & Related papers  (2024-05-20T18:15:20Z)
• Blind deblurring of hyperspectral document images [0.0]
  Multispectral (MS) and hyperspectral (HS) images contain much richer spectral information than RGB images. We propose novel blind HS image deblurring methods tailored to document images.
  arXiv  Detail & Related papers  (2023-03-09T09:31:13Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• Multi-modal Aggregation Network for Fast MR Imaging [85.25000133194762]
  We propose a novel Multi-modal Aggregation Network, named MANet, which is capable of discovering complementary representations from a fully sampled auxiliary modality. In our MANet, the representations from the fully sampled auxiliary and undersampled target modalities are learned independently through a specific network. Our MANet follows a hybrid domain learning framework, which allows it to simultaneously recover the frequency signal in the $k$-space domain.
  arXiv  Detail & Related papers  (2021-10-15T13:16:59Z)
• Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild [48.44194221801609]
  We propose a new lightweight and end-to-end learning-based framework to tackle this challenge. We progressively spread the differences between input RGB images and re-projected RGB images from recovered HS images via effective camera spectral response function estimation. Our method significantly outperforms state-of-the-art unsupervised methods and even exceeds the latest supervised method under some settings.
  arXiv  Detail & Related papers  (2021-08-15T05:19:44Z)
• Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization [9.584717030078245]
  Hyperspectral (HS) images contain detailed spectral information that has proven crucial in applications like remote sensing, surveillance, and astronomy. Because of hardware limitations of HS cameras, the captured images have low spatial resolution. To improve them, the low-resolution hyperspectral images are fused with conventional high-resolution RGB images via a technique known as fusion based HS image super-resolution.
  arXiv  Detail & Related papers  (2021-06-13T18:52:47Z)
• Tuning IR-cut Filter for Illumination-aware Spectral Reconstruction from RGB [84.1657998542458]
  It has been proven that the reconstruction accuracy relies heavily on the spectral response of the RGB camera in use. This paper explores the filter-array based color imaging mechanism of existing RGB cameras, and proposes to design the IR-cut filter properly for improved spectral recovery.
  arXiv  Detail & Related papers  (2021-03-26T19:42:21Z)
• Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery [79.69449412334188]
  In this paper, we investigate how to adapt state-of-the-art residual learning based single gray/RGB image super-resolution approaches. We introduce a spatial-spectral prior network (SSPN) to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data. Experimental results on some hyperspectral images demonstrate that the proposed SSPSR method enhances the details of the recovered high-resolution hyperspectral images.
  arXiv  Detail & Related papers  (2020-05-18T14:25:50Z)
• Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
  We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output. We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
  arXiv  Detail & Related papers  (2020-03-15T23:07:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.