SASSI -- Super-Pixelated Adaptive Spatio-Spectral Imaging

• URL: http://arxiv.org/abs/2012.14495v1
• Date: Mon, 28 Dec 2020 21:34:18 GMT
• Title: SASSI -- Super-Pixelated Adaptive Spatio-Spectral Imaging
• Authors: Vishwanath Saragadam, Michael DeZeeuw, Richard Baraniuk, Ashok Veeraraghavan, and Aswin Sankaranarayanan
• Abstract summary: We introduce a novel video-rate hyperspectral imager with high spatial, and temporal resolutions. A scene-adaptive spatial sampling of an hyperspectral scene, guided by its super-pixel segmented image, is capable of obtaining high-quality reconstructions. We validate the proposed technique with extensive simulations as well as a lab prototype that measures hyperspectral video at a spatial resolution of $600 times 900$ pixels.
• Score: 17.2152544145501
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a novel video-rate hyperspectral imager with high spatial, and temporal resolutions. Our key hypothesis is that spectral profiles of pixels in a super-pixel of an oversegmented image tend to be very similar. Hence, a scene-adaptive spatial sampling of an hyperspectral scene, guided by its super-pixel segmented image, is capable of obtaining high-quality reconstructions. To achieve this, we acquire an RGB image of the scene, compute its super-pixels, from which we generate a spatial mask of locations where we measure high-resolution spectrum. The hyperspectral image is subsequently estimated by fusing the RGB image and the spectral measurements using a learnable guided filtering approach. Due to low computational complexity of the superpixel estimation step, our setup can capture hyperspectral images of the scenes with little overhead over traditional snapshot hyperspectral cameras, but with significantly higher spatial and spectral resolutions. We validate the proposed technique with extensive simulations as well as a lab prototype that measures hyperspectral video at a spatial resolution of $600 \times 900$ pixels, at a spectral resolution of 10 nm over visible wavebands, and achieving a frame rate at $18$fps.

Related papers

• HyperColorization: Propagating spatially sparse noisy spectral clues for reconstructing hyperspectral images [2.7214317850962106]
  We present a colorization to reconstruct hyperspectral images from a grayscale guide image and spatially sparse spectral clues. Our algorithm generalizes to varying spectral dimensions for hyperspectral images, and show that colorizing in a low-rank space reduces compute time and the impact of shot noise.
  arXiv  Detail & Related papers  (2024-03-18T16:33:43Z)
• SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution [73.46167948298041]
  We propose a neural implicit model that represents an image as a function of both continuous pixel coordinates in the spatial domain and continuous wavelengths in the spectral domain. We show that SSIF generalizes well to both unseen spatial resolutions and spectral resolutions. It can generate high-resolution images that improve the performance of downstream tasks by 1.7%-7%.
  arXiv  Detail & Related papers  (2023-09-30T15:23:30Z)
• High Spectral Spatial Resolution Synthetic HyperSpectral Dataset form multi-source fusion [7.249349307341409]
  This research paper introduces a synthetic hyperspectral dataset that combines high spectral and spatial resolution imaging. The proposed dataset addresses this limitation by leveraging three modalities: RGB, push-broom visible hyperspectral camera, and snapshot infrared hyperspectral camera.
  arXiv  Detail & Related papers  (2023-06-25T11:17:12Z)
• Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation [55.9217962930169]
  We present S2ADet, an object detector that harnesses the rich spectral and spatial complementary information inherent in hyperspectral images. S2ADet surpasses existing state-of-the-art methods, achieving robust and reliable results.
  arXiv  Detail & Related papers  (2023-06-14T09:01:50Z)
• Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion [67.35540259040806]
  We propose a subpixel-level HS super-resolution framework by devising a novel decoupled-and-coupled network, called DCNet. As the name suggests, DC-Net first decouples the input into common (or cross-sensor) and sensor-specific components. We append a self-supervised learning module behind the CSU net by guaranteeing the material consistency to enhance the detailed appearances of the restored HS product.
  arXiv  Detail & Related papers  (2022-05-07T23:40:36Z)
• Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry [6.279792995020646]
  This work introduces the Spatial-Spectral Image Reconstruction and Clustering with Diffusion Geometry (DSIRC) algorithm for partitioning highly mixed hyperspectral images. DSIRC locates spectrally correlated pixels within a data-adaptive spatial neighborhood and reconstructs that pixel's spectral signature using those of its neighbors. Results indicate that incorporating spatial information through image reconstruction substantially improves the performance of pixel-wise clustering.
  arXiv  Detail & Related papers  (2022-04-28T13:42:12Z)
• Superpixel Segmentation Based on Spatially Constrained Subspace Clustering [57.76302397774641]
  We consider each representative region with independent semantic information as a subspace, and formulate superpixel segmentation as a subspace clustering problem. We show that a simple integration of superpixel segmentation with the conventional subspace clustering does not effectively work due to the spatial correlation of the pixels. We propose a novel convex locality-constrained subspace clustering model that is able to constrain the spatial adjacent pixels with similar attributes to be clustered into a superpixel.
  arXiv  Detail & Related papers  (2020-12-11T06:18:36Z)
• Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning [62.52242684874278]
  Cross-modality distribution of spatial and spectral information makes the problem challenging. We propose a novel textitlightweight deep neural network-based framework, namely PZRes-Net. Our framework learns a high resolution and textitzero-centric residual image, which contains high-frequency spatial details of the scene.
  arXiv  Detail & Related papers  (2020-06-18T06:32:11Z)
• Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array [1.6058099298620423]
  Hyperspectral imaging is useful for applications ranging from medical diagnostics to agricultural crop monitoring. Traditional hyperspectral imagers are prohibitively slow and expensive for widespread adoption. We propose a compact, compact and inexpensive camera for hyperspectral imaging.
  arXiv  Detail & Related papers  (2020-06-15T17:31:17Z)
• 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)

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.