Frequency of metamerism in natural scenes

Foster, David H.; Amano, Kinjiro; Nascimento, Sérgio M. C.; Foster, Michael J.

doi:10.1364/josaa.23.002359

Cited by 268 publications

(155 citation statements)

References 34 publications

(57 reference statements)

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“…In this section, the performance of CASSI sensing matrix is evaluated in the reconstruction of three spectral images (Foster et al, 2006). An example of the first database of 533 nm is shown in Figure 8a.…”

Section: Experiments With Real Datamentioning

confidence: 99%

Probability of correct reconstruction in compressive spectral imaging

2016

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Coded Aperture Snapshot Spectral Imaging (CASSI) systems capture the 3-dimensional (3D) spatio-spectral information of a scene using a set of 2-dimensional (2D) random coded Focal Plane Array (FPA) measurements. A compressed sensing reconstruction algorithm is then used to recover the underlying spatio-spectral 3D data cube. The quality of the reconstructed spectral images depends exclusively on the CASSI sensing matrix, which is determined by the statistical structure of the coded apertures. The Restricted Isometry Property (RIP) of the CASSI sensing matrix is used to determine the probability of correct image reconstruction and provides guidelines for the minimum number of FPA measurement shots needed for image reconstruction. Further, the RIP can be used to determine the optimal structure of the coded projections in CASSI. This article describes the CASSI optical architecture and develops the RIP for the sensing matrix in this system. Simulations show the higher quality of spectral image reconstructions when the RIP property is satisfied. Simulations also illustrate the higher performance of the optimal structured projections in CASSI.Keywords: Restricted Isometry Property, RIP, CASSI, compressive sensing, spectral imaging, coded aperture. RESUMENEl sistema de adquisición de imágenes espectrales de única captura basado en apertura codificada (CASSI), capta información tridimensional (3D) espacio-espectral de una escena, usando un conjunto de medidas bidimensionales (2D) proyectadas en un FPA (Focal Plane Array). Para recuperar el cubo de datos a partir de las proyecciones en el FPA, se usa un algoritmo de reconstrucción basado en la teoría de muestreo compresivo. En CASSI la calidad de la reconstrucción de imágenes espectrales depende exclusivamente de la matriz de sensado, que es determinada por la estructura estadística del código de apertura. La propiedad restringida isométrica (RIP) de la matriz de sensado CASSI es usada para determinar la probabilidad de una correcta reconstrucción de la imagen. Este artículo describe la arquitectura óptima CASSI y desarrolla la RIP para las matrices de muestreo, para la captura de la información del cubo de datos. En efecto, la RIP provee la guía para determinar el mínimo número de capturas FPA necesarias para la reconstrucción de una imagen. Más adelante, la RIP es usada para encontrar la estructura óptima de las proyecciones de los códigos de apertura de CASSI. Las simulaciones muestran alta calidad de la reconstrucción obtenida de las imágenes espectrales cuando se satisface la condición impuesta por la RIP. También muestran el más alto rendimiento obtenido de las estructuras óptimas de las proyecciones CASSI.

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Section: Experiments With Real Datamentioning

confidence: 99%

Probability of correct reconstruction in compressive spectral imaging

2016

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“…Based on the above procedure the hyper spectral images of 2 different scenes [7] are compressed and shown in Fig3 …”

Section: Resultsmentioning

confidence: 99%

Hyper spectral image compression based on non-iterative matrix factorization

Nallathai

Jeyakumar²,

Nithiyanandam

2013

2013 IEEE International Conference on Computational Intelligence and Computing Research

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Hyper spectral images provide a more detailed information than multispectral images, as every pixel in the image contains contiguous spectral bands to characterize the details in the scene. Since hyper spectral images occupy large memory space and take more processing time for the transmission, it is highly desirable to use an efficient compression technique. In this paper we discuss hyper spectral image compression using matrix factorization based on a proposed non-iterative method and compare with the Tucker decomposition for hyper spectral image compression. The proposed non-iterative positive matrix factorization method is based on least mean square error (LMSE) criterion. Results indicate that hyper spectral image compression based on non-iterative method of matrix factorization needs less processing time, compared with compression based on Tucker decomposition.

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“…1 a and b). Further, due to the limited number of the spectral channels over spectrum (three in the conventional RGB camera and human eye), such imaging device cannot always produce correct colour appearance of some surfaces which has complex spectral properties are, known as metameric surfaces [20]. …”

Section: Colour Image For Materials Identificationmentioning

confidence: 99%

“…The hyperspectral imaging has evolved to include not just one monochromatic channel or three colour channels covering the visible spectrum, but many channels, over 30, even several hundreds, extending from the visible spectrum to near-infrared (NIR) and shortwave infrared (SWIR) channels [20,23].…”

Section: Hyperspectral Imagingmentioning

confidence: 99%

BIM for existing facilities: feasibility of spectral image integration to 3D point cloud data

Amano

Lou

2016

MATEC Web Conf.

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Abstract. Accurate geometrical and spatial information of the built environment can be accurately acquired and the resulting 3D point cloud data is required to be processed to construct the digital model, Building Information Modelling (BIM) for existing facilities. Point cloud by laser scanning over the buildings and facilities has been commonly used, but the data requires external information so that any objects and materials can be correctly identified and classified. A number of advanced data processing methods have been developed, such as the use of colour information to attach semantic information. However, the accuracy of colour information depends largely on the scene environment where the image is acquired. The limited number of spectral channels on conventional RGB camera often fails to extract important information about surface material, despite spectral surface reflectance can represent a signature of the material. Hyperspectral imaging can, instead, provide precise representation of spatial and spectral information. By implementing such information to 3D point cloud, the efficiency of material detection and classification in BIM should be significantly improved. In this work, the feasibility of the image integration and discuss practical difficulties in the development.

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Frequency of metamerism in natural scenes

Cited by 268 publications

References 34 publications

Probability of correct reconstruction in compressive spectral imaging

Probability of correct reconstruction in compressive spectral imaging

Hyper spectral image compression based on non-iterative matrix factorization

BIM for existing facilities: feasibility of spectral image integration to 3D point cloud data

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