High-fidelity video and still-image communication based on spectral information: natural vision system and its applications

Yamaguchi, Masahiro; Haneishi, Hideaki; Fukuda, Hiroyuki; Kishimoto, Junko; Kanazawa, Hiroshi; Tsuchida, Masanori; Iwama, Ryo; Ohyama, Nagaaki

doi:10.1117/12.649454

Cited by 43 publications

(15 citation statements)

References 29 publications

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“…Spectral scanning approaches employ multiple color band-pass filters to record the different spectral bands of the spectral datacube. For example, a rotating filter wheel [Yamaguchi et al 2006] or tunable spectral filter [Gat 2000] can be placed in front of the camera to change the spectral bands of the capture images; or a spatial variant color filter is distributed over the sensor which requires multiple exposures to record different spectral band at each pixel [Schechner and Nayar 2002].…”

Section: Related Workmentioning

confidence: 99%

“…Traditional HS imaging approaches involve a temporal sequential scanning of either a spatial [Porter and Enmark 1987;Basedow et al 1995] or spectral dimension [Yamaguchi et al 2006;Gat 2000;Schechner and Nayar 2002]. Spatial scanning approaches, such as whiskbroom [Porter and Enmark 1987] or pushbroom [Basedow et al 1995], capture the spectrum of a single scene point or a slit of the scene, and scan the entire scene to obtain a complete datacube.…”

Section: Related Workmentioning

confidence: 99%

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Spatial-spectral encoded compressive hyperspectral imaging

et al. 2014

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Figure 1: 3D hyperspectral (HS) images reconstructed from a single spatial-spectral encoded 2D projection for an outdoor scene under sunlight environment. Left-most: The sensor image captured by our camera prototype. Second-by-left: Recovered high-spatial resolution 3D HS images with color indicating 31 spectral bands from 420nm to 720nm. Third and fourth: Closeup of recovered 520nm and 650nm spectral bands images. Right-most: The synthetic RGB image from the reconstructed HS images. AbstractThis paper proposes a novel compressive hyperspectral (HS) imaging approach that allows for high-resolution HS images to be captured in a single image. The proposed architecture comprises three key components: spatial-spectral encoded optical camera design, over-complete HS dictionary learning and sparse-constraint computational reconstruction. Our spatial-spectral encoded sampling scheme provides a higher degree of randomness in the measured projections than previous compressive HS imaging approaches; and a robust nonlinear sparse reconstruction method is employed to recover the HS images from the coded projection with higher performance. To exploit the sparsity constraint on the nature HS images for computational reconstruction, an over-complete HS dictionary is learned to represent the HS images in a sparser way than previous representations. We validate the proposed approach on both synthetic and real captured data, and show successful recovery of HS images for both indoor and outdoor scenes. In addition, we demonstrate other applications for the over-complete HS dictionary and sparse coding techniques, including 3D HS images compression and denoising.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Spatial-spectral encoded compressive hyperspectral imaging

et al. 2014

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“…A noteworthy approach that avoids this trade-off is the IRODORI system that can capture 6-band HDTV video using an optical splitter, color filters, and two RGB sensors [30]. However, the system does not deal with how to recover the continuous spectrum from the 6 color measurements.…”

Section: Related Workmentioning

confidence: 99%

Multispectral Imaging Using Multiplexed Illumination

Park

Lee

Grossberg

et al. 2007

2007 IEEE 11th International Conference on Computer Vision

250

171

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Many vision tasks such as scene segmentation, or the recognition of materials within a scene, become considerably easier when it is possible to measure the spectral reflectance of scene surfaces. In this paper, we present an efficient and robust approach for recovering spectral reflectance in a scene that combines the advantages of using multiple spectral sources and a multispectral camera. We have implemented a system based on this approach using a cluster of light sources with different spectra to illuminate the scene and a conventional RGB camera to acquire images. Rather than sequentially activating the sources, we have developed a novel technique to determine the optimal multiplexing sequence of spectral sources so as to minimize the number of acquired images. We use our recovered spectral measurements to recover the continuous spectral reflectance for each scene point by using a linear model for spectral reflectance. Our imaging system can produce multispectral videos of scenes at 30fps. We demonstrate the effectiveness of our system through extensive evaluation. As a demonstration, we present the results of applying data recovered by our system to material segmentation and spectral relighting.

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“…One of the features of Natural Vision is that the use of a multi-spectral camera and a multiprimary display allows natural color reproduction which is far beyond the capacity of the conventional RGB (red-greenblue) system [2]. Most existing display devices utilize three primary colors: red, green, and blue (RGB).…”

Section: Introductionmentioning

confidence: 99%

A subjective evaluation of high-chroma color with wide color-gamut display

2009

Self Cite

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Displays tends to expand its color gamut, such as multi-primary color display, Adobe RGB and so on. Therefore displays got possible to display high chroma colors. However sometimes, we feel unnatural some for the image which only expanded chroma. Appropriate gamut mapping method to expand color gamut is not proposed very much. We are attempting preferred expanded color reproduction on wide color gamut display utilizing high chroma colors effectively. As a first step, we have conducted an experiment to investigate the psychological effect of color schemes including highly saturated colors. We used the six-primary-color projector that we have developed for the presentation of test colors. The six-primary-color projector's gamut volume in CIELAB space is about 1.8 times larger than the normal RGB projector. We conducted a subjective evaluation experiment using the SD (Semantic Differential) technique to find the quantitative psychological effect of high chroma colors.

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High-fidelity video and still-image communication based on spectral information: natural vision system and its applications

Cited by 43 publications

References 29 publications

Spatial-spectral encoded compressive hyperspectral imaging

Spatial-spectral encoded compressive hyperspectral imaging

Multispectral Imaging Using Multiplexed Illumination

A subjective evaluation of high-chroma color with wide color-gamut display

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