Proceedings ArticleDOI
2D-3D CNN Based Architectures for Spectral Reconstruction from RGB Images
Sriharsha Koundinya,Himanshu Sharma,Manoj Sharma,Avinash Upadhyay,Raunak Manekar,Rudrabha Mukhopadhyay,Abhijit Karmakar,Santanu Chaudhury +7 more
- pp 844-851
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TLDR
This work proposes a 2D convolution neural network and a 3D convolved neural network based approaches for hyperspectral image reconstruction from RGB images that achieves very good performance in terms of MRAE and RMSE.Abstract:
Hyperspectral cameras are used to preserve fine spectral details of scenes that are not captured by traditional RGB cameras that comprehensively quantizes radiance in RGB images. Spectral details provide additional information that improves the performance of numerous image based analytic applications, but due to high hyperspectral hardware cost and associated physical constraints, hyperspectral images are not easily available for further processing. Motivated by the performance of deep learning for various computer vision applications, we propose a 2D convolution neural network and a 3D convolution neural network based approaches for hyperspectral image reconstruction from RGB images. A 2D-CNN model primarily focuses on extracting spectral data by considering only spatial correlation of the channels in the image, while in 3D-CNN model the inter-channel co-relation is also exploited to refine the extraction of spectral data. Our 3D-CNN based architecture achieves very good performance in terms of MRAE and RMSE. In contrast to 3D-CNN, our 2D-CNN based architecture also achieves comparable performance with very less computational complexity.read more
Citations
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Proceedings ArticleDOI
lambda-Net: Reconstruct Hyperspectral Images From a Snapshot Measurement
TL;DR: The λ-net, which reconstructs hyperspectral images from a single shot measurement, can finish the reconstruction task within sub-seconds instead of hours taken by the most recently proposed DeSCI algorithm, thus speeding up the reconstruction >1000 times.
Proceedings ArticleDOI
NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images
TL;DR: This paper reviews the first challenge on spectral image reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image.
Book ChapterDOI
End-to-End Low Cost Compressive Spectral Imaging with Spatial-Spectral Self-Attention
Ziyi Meng,Jiawei Ma,Xin Yuan +2 more
TL;DR: This work reproduces a stable single disperser CASSI system and proposes a novel deep convolutional network to carry out the real-time reconstruction by using self-attention, employing Spatial-Spectral Self-Attention (TSA) to process each dimension sequentially, yet in an order-independent manner.
Proceedings ArticleDOI
Adaptive Weighted Attention Network With Camera Spectral Sensitivity Prior for Spectral Reconstruction From RGB Images
TL;DR: Zhang et al. as mentioned in this paper proposed an adaptive weighted channel attention (AWCA) module to reallocate channel-wise feature responses via integrating correlations between channels, and a patch-level second-order non-local (PSNL) module is developed to capture long-range spatial contextual information.
Proceedings ArticleDOI
Hierarchical Regression Network for Spectral Reconstruction from RGB Images
TL;DR: A 4-level Hierarchical Regression Network (HRNet) with PixelShuffle layer as inter-level interaction with a residual dense block to remove artifacts of real world RGB images and a residual global block to build attention mechanism for enlarging perceptive field is proposed.
References
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Journal ArticleDOI
Visual enhancement of old documents with hyperspectral imaging
TL;DR: This work demonstrates how to use the invisible bands to improve the visual quality of text-based documents corrupted with undesired artifacts such as ink-bleed, ink-corrosion, and foxing.
MonographDOI
Spectrograph design fundamentals
TL;DR: A brief history of spectroscopy can be found in this article, where the relevant regions of the electromagnetic spectrum are discussed and a brief review of spectrograph design and construction is given.
Proceedings ArticleDOI
Hyperspectral Face Recognition using 3D-DCT and Partial Least Squares
TL;DR: The three dimensional Discrete Cosine Transform (3D-DCT) is proposed for feature extraction and it is shown that compared to other transforms, such as the Fourier transform, the transformed coefficients are real and thus require less data to process.
Posted Content
Learned Spectral Super-Resolution.
TL;DR: A novel method for blind, single-image spectral super-resolution, which starts from the conjecture that it can learn the statistics of natural image spectra, and with its help generate finely resolved hyper-spectral images from RGB input.
Proceedings ArticleDOI
HyperCam: hyperspectral imaging for ubiquitous computing applications
Mayank Goel,Eric Whitmire,Alex Mariakakis,T. Scott Saponas,Neel Joshi,Dan Morris,Brian Guenter,Marcel Gavriliu,Gaetano Borriello,Shwetak N. Patel +9 more
TL;DR: HyperCam provides a low-cost implementation of a multispectral camera and a software approach that automatically analyzes the scene and provides a user with an optimal set of images that try to capture the salient information of the scene.