Higher-order computational model for coded aperture spectral imaging

Argüello, Henry; Rueda, Hoover; Wu, Yuehao; Prather, Dennis W.; Arce, Gonzalo R.

doi:10.1364/ao.52.000d12

Cited by 116 publications

(48 citation statements)

References 8 publications

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“…A high pass filter H permits to pass the wavelengths λ 2 , λ 3 and λ 4 . The use of this optical element has demonstrated a high probability of correct reconstruction (high PSNR) in previous researches [17,18].…”

Section: Figure 5 Algorithm 2 Reconstruction Process Of a Multispectmentioning

confidence: 89%

Reconstruction of multispectral light field (5d plenoptic function) based on compressive sensing with colored coded apertures from 2D projections

León-López

Galvis-Carreño

Arguello-Fuentes

2016

Rev. Fac. Ing. Univ. Antioquia

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ABSTRACT:In the last decade, spatio -angular (light field) acquisition systems have advanced due to the inclusion of coded apertures in the optical path. These coded apertures, modulate the light, encoding the information before being captured. Traditionally, these coded apertures are binary, i.e. block and unblock the light rays in the spatial dimension, capturing sparse information of the scene. In this work, the binary coded aperture is replaced by a colored coded aperture which modulates the source not only spatially but spectrally. Thereby, it is possible to capture light fields in multiple wavelengths yielding high spectral resolution. The spectral information provides many features of a scene in different wavelengths, these features are not present in the visible range of the electromagnetic spectrum. In this paper, an algorithm that simulates the light field sampling with colored coded apertures is proposed. The proposed algorithm, exploits the redundant information of the scene based on the compressive sensing theory thus, capturing just a sparse signal. The multidimensional image can be recovered from the underlying signal through a reconstruction algorithm. Several simulations show the quality of the multispectral light field reconstructions. The PSNR (Peak Signal to Noise Ratio) values obtained for the reconstructions are around 25 dB.

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Section: Figure 5 Algorithm 2 Reconstruction Process Of a Multispectmentioning

confidence: 89%

Reconstruction of multispectral light field (5d plenoptic function) based on compressive sensing with colored coded apertures from 2D projections

León-López

Galvis-Carreño

Arguello-Fuentes

2016

Rev. Fac. Ing. Univ. Antioquia

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“…optical system. There, it can be seen that the single voxel spreads onto more than a single FPA pixel due to the continuous nature of the dispersion function s(λ) [9]. Mathematically, the portion of the voxel that impinges in each FPA pixel can be written as,…”

Section: Higher-order Discrete Model Approximationmentioning

confidence: 99%

High-dimensional optimization of color coded apertures for compressive spectral cameras

Rueda

Argüello

Arce

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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A spectral image can be regarded as a three-dimensional cube where each pixel is a vector of intensities representing a spectral signature. Compressive spectral imaging (CSI) is a sensing and reconstruction framework, based on the fundamentals of the compressive sensing theory, which focuses on capturing spectral images efficiently, exploiting their highly correlated information by coding its spectral characteristics commonly using a black-and-white, grayscale or recently a color coded aperture. The distribution of the entries of the coded apertures determines the quality of the estimated spectral images. State of the art methods have used random coded apertures, and some optimization procedures have focused on the optimal design of horizontal sections of the coded apertures; however, they do not fully exploit the spatio-spectral correlations within the spectral images. To that end, in this paper, it is proposed a high-dimensional optimization procedure to design color coded apertures for CSI systems, which exploits not only the spectral correlations but also the spatial correlations within an spectral image. Simulations analyzing the conditioning of the sensing matrices, as well as the reconstruction quality of the attained spectral images show the improvement entailed by the proposed method.Index Terms-Compressive spectral imaging, coded aperture design, color filter array, numerical optimization.

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“…More specifically, CSI establishes that it is possible to retrieve a spectral image from a small number of samples, under the assumption that it has a sparse representation in some basis Ψ Ψ Ψ. In particular, a spectral image f ∈ R M ·N ·L has a dispersion level S, if it can be represented as a linear combination of S vectors on any basis Ψ, such that f = Ψθ with S M N L. Thus, instead of acquiring M N L samples, CS captures K M N L random projections of the scene, where K is not necessarily equal to the sparsity level S. The sensing process can be represented in matrix form as g = Hf , where H is the transfer matrix of the system [5]. Because the number of measurements is considerably less than the number of voxels, the inverse problem given by f = H −1 g, is ill conditioned, leading to an infinite number of solutions.…”

Section: Introductionmentioning

confidence: 99%

Multi-resolution reconstruction algorithm for compressive single pixel spectral imaging

García

Correa

Villarreal

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Spectral imaging is useful in a wide range of applications for non-invasive detection and classification. However, the massive amount of involved data increases its processing and storing costs. In contrast, compressive spectral imaging (CSI) establishes that the three-dimensional data cube can be recovered from a small set of projections, that are generally captured in 2-dimensional detectors. Furthermore, the single-pixel camera (SPC) has been also employed for spectral imaging. Specifically, the SPC captures the spatial and spectral information in a single measurement. CSI reconstructions are traditionally obtained by solving a minimization problem using iterative algorithms. However, the computational load of these algorithms is high due to the dimensionality of the involved sensing matrices. In this paper, a multi-resolution (MR) reconstruction model is proposed such that the complexity of the inverse problem is reduced. In particular, this model uses the spectral correlation to group pixels with similar spectral characteristics. Simulation results show that the MR model improves the reconstruction PSNR in up to 9dB with respect to the traditional methods. In addition, the proposed approach is 79% faster, using only 25% of the measurements.

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Higher-order computational model for coded aperture spectral imaging

Cited by 116 publications

References 8 publications

Reconstruction of multispectral light field (5d plenoptic function) based on compressive sensing with colored coded apertures from 2D projections

Reconstruction of multispectral light field (5d plenoptic function) based on compressive sensing with colored coded apertures from 2D projections

High-dimensional optimization of color coded apertures for compressive spectral cameras

Multi-resolution reconstruction algorithm for compressive single pixel spectral imaging

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