Rank Minimization Code Aperture Design for Spectrally Selective Compressive Imaging

Argüello, Henry; Arce, Gonzalo R.

doi:10.1109/tip.2012.2222899

Cited by 89 publications

(43 citation statements)

References 14 publications

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“…Specifically, for spectral video, the basis Ψ can be constructed as a Kronecker product between different basis as Ψ = Ψ 1 ⊗ Ψ 2 ⊗ Ψ 3 ⊗ Ψ 4 , such that the four dimensional basis exploits the correlation between the spatial, the spectral and the temporal information of the spectral video [2]. Commonly used bases include Wavelet, Cosine, Curvelet transforms and trained dictionaries [16], [17].…”

Section: A Mathematical Sampling Modelmentioning

confidence: 99%

Spatio-spectro-temporal coded aperture design for multiresolution compressive spectral video sensing

Lopez

Galvis

Fuentes

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Colored coded apertures (CCA) have been introduced in compressive spectral imaging (CSI) systems entailing richer coding strategies. CCA incorporate a wavelengthdependent coding procedure, which not only achieves spatial but also spectral coding in a single step. Even though the use of the CCA offers significant advantages and could also be applied to compressive spectral video sensing, this later application still exhibits diverse challenges originated by the temporal variable. The scene motion during the acquisition yields to motion artifacts such that these artifacts get aliased during the video reconstruction, damaging the entire data. As a result, multiresolution approaches have been proposed in order to alleviate the aliasing and enhance the video reconstruction. In this paper, it is proposed an algorithm to generate temporal colored coded aperture patterns that allow to sense the spatial, spectral and temporal information in an uniform way such that each spectral frame is spatially sensed at least once. In addition, it is proposed a multiresolution approach in the spectral multiplexing system allowing to extract optical flow estimates to address a higher quality reconstruction. Simulation results show an improvement up to 6 dB in terms of peak-signal to noise ratio (PSNR) in the reconstruction quality with the multiresolution approach using the designed patterns with respect to traditional random structures.

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Section: A Mathematical Sampling Modelmentioning

confidence: 99%

Spatio-spectro-temporal coded aperture design for multiresolution compressive spectral video sensing

Lopez

Galvis

Fuentes

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…Capturing multiple snapshots from a scene provides additional information that yields improved signal reconstruction [12]. The process of capturing K snapshots can be represented in the standard form of an undetermined system of linear equations as follows…”

Section: The Coded Aperture Snapshot Spectral Imager Sensing Processmentioning

confidence: 99%

“…A 3-dimensional (3D) data cube is commonly recovered from coded projections by solving a compressive-sensing-based minimization algorithm, which assumes that the scene is highly compressible when represented in some known orthonormal basis [10]- [12]. The matrix completion (MC) framework introduced in [13], [14] does not require prior knowledge since it relies on a low-rank matrix structure to recover the scene from a small subset of accurately observed entries [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Recovering spectral images from compressive measurements using designed coded apertures and Matrix Completion Theory

Gelvez

Rueda

Argüello

2017

I&U:EfD

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<p>Spectral imaging aims to capture and process a 3-dimensional spectral image with a large amount of spectral information for each spatial location. Compressive spectral imaging techniques (CSI) increases the sensing speed and reduces the amount of collected data compared to traditional spectral imaging methods. The coded aperture snapshot spectral imager (CASSI) is an optical architecture to sense a spectral image in a single 2D coded projection by applying CSI. Typically, the 3D scene is recovered by solving an L1-based optimization problem that assumes the scene is sparse in some known orthonormal basis. In contrast, the matrix completion technique (MC) allows to recover the scene without such prior knowledge. The MC reconstruction algorithms rely on a low-rank structure of the scene. Moreover, the CASSI system uses coded aperture patterns that determine the quality of the estimated scene. Therefore, this paper proposes the design of an optimal coded aperture set for the MC methodology. The designed set is attained by maximizing the distance between the translucent elements in the coded aperture. Visualization of the recovered spectral signals and simulations over different databases show average improvement when the designed coded set is used between 1-3 dBs compared to the complementary coded aperture set, and between 3-9 dBs compared to the conventional random coded aperture set.</p>

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“…Optimization of black-and-white coded apertures has been demonstrated in the past [6]. Regarding color coded apertures, a single work have reported their optimization [4], but focusing only on row-wise optimization, which does not fully exploit the spatial correlation within spectral images.…”

Section: Introductionmentioning

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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Rank Minimization Code Aperture Design for Spectrally Selective Compressive Imaging

Cited by 89 publications

References 14 publications

Spatio-spectro-temporal coded aperture design for multiresolution compressive spectral video sensing

Spatio-spectro-temporal coded aperture design for multiresolution compressive spectral video sensing

Recovering spectral images from compressive measurements using designed coded apertures and Matrix Completion Theory

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

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