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Open access publications by faculty, postdocs, and graduate students in the Department of Electrical and Computer Engineering
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Browsing Open Access Publications by Author "Arce, Gonzalo R."
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Item Block-based spectral image reconstruction for compressive spectral imaging using smoothness on graphs(Optics Express, 2022-02-17) Florez-Ospina, Juan F.; Alrushud, Abdullah K. M.; Lau, Daniel L.; Arce, Gonzalo R.A novel reconstruction method for compressive spectral imaging is designed by assuming that the spectral image of interest is sufficiently smooth on a collection of graphs. Since the graphs are not known in advance, we propose to infer them from a panchromatic image using a state-of-the-art graph learning method. Our approach leads to solutions with closed-form that can be found efficiently by solving multiple sparse systems of linear equations in parallel. Extensive simulations and an experimental demonstration show the merits of our method in comparison with traditional methods based on sparsity and total variation and more recent methods based on low-rank minimization and deep-based plug-and-play priors. Our approach may be instrumental in designing efficient methods based on deep neural networks and covariance estimation.Item Compressive Spectral Imaging via Misalignment Induced Equivalent Grayscale Coded Aperture(IEEE Geoscience and Remote Sensing Letters, 2023-02-22) Zhang, Tong; Zhao, Shengjie; Ma, Xu; Ramirez-Jaime, Andres; Zhao, Qile; Arce, Gonzalo R.Coded aperture snapshot spectral imager (CASSI) senses the spectral information of a 2-D scene and captures a set of coded measurement data that can be used to reconstruct the 3-D spatio-spectral datacube of the input scene by compressive sensing algorithms. The coded aperture (CA) in CASSI plays a crucial role in modulating the spatial information. The pixels in CA are typically square, switched binary ON–OFF, and aligned with the pixels of focal plane array (FPA). Instead of this binary modulation, this letter explores a simple yet effective approach to enabling an equivalent grayscale modulation, which can increase the sensing degree of freedom in CASSI systems. In particular, we deliberately introduce misalignment between the CA pixels and the FPA pixels, such that the spatial modulation of one FPA pixel is determined by four adjacent CA pixels instead of one. Numerical experiments show that the proposed equivalent grayscale modulation induced by misalignment can significantly improve the CASSI reconstruction when compared with current methods, whether a random CA or an optimal blue noise CA is used. More importantly, it does not incur in any cost to the CASSI system.Item Compressive Spectral X-Ray CT Reconstruction via Deep Learning(IEEE Transactions on Computational Imaging, 2022-10-20) Zhang, Tong; Zhao, Shengjie; Ma, Xu; Restrepo, Carlos; Arce, Gonzalo R.Compressive spectral X-ray imaging (CSXI) uses coded illumination projections to reconstruct tomographic images at multiple energy bins. Different K-edge materials are used to modulate the spectrum of the X-ray source at various angles, thereby capturing the projections containing spectral attenuation information. It is a cost-effective and low-dose sensing approach; however, the image reconstruction is a nonlinear and ill-posed problem. Current methods of solving the inverse problem are computationally expensive and require extensive iterations. This paper proposes a deep learning model consisting of a set of convolutional neural networks to reconstruct the CSXI spectral images, which correspond to inpainting the subsampled sinograms, recovering the monoenergetic sinograms, and removing the artifacts from a fast but low-quality analytical reconstruction. Numerical experiments show that the proposed method significantly improves the quality of reconstructed image compared with that attained by the state-of-the-art reconstruction methods. Moreover, it significantly reduces the time-required for CSXI reconstruction.Item Experimental demonstration and optimization of X-ray StaticCodeCT(Applied Optics, 2021-10-18) Cuadros, Angela P.; Liu, Xiaokang; Parsons, Paul E.; Ma, Xu; Arce, Gonzalo R.As the use of X-ray computed tomography (CT) grows in medical diagnosis, so does the concern for the harm a radiation dose can cause and the biological risks it represents. StaticCodeCT is a new low-dose imaging architecture that uses a single-static coded aperture (CA) in a CT gantry. It exploits the highly correlated data in the projection domain to estimate the unobserved measurements on the detector. We previously analyzed the StaticCodeCT system by emulating the effect of the coded mask on experimental CT data. In contrast, this manuscript presents test-bed reconstructions using an experimental cone-beam X-ray CT system with a CA holder. We analyzed the reconstruction quality using three different techniques to manufacture the CAs: metal additive manufacturing, cold casting, and ceramic additive manufacturing. Furthermore, we propose an optimization method to design the CA pattern based on the algorithm developed for the measurement estimation. The obtained results point to the possibility of the real deployment of StaticCodeCT systems in practice.Item Hyperspectral image reconstruction via patch attention driven network(Optics Express, 2023-06-01) Qiu, Yechuan; Zhao, Shengjie; Ma, Xu; Zhang, Tong; Arce, Gonzalo R.Coded aperture snapshot spectral imaging (CASSI) captures 3D hyperspectral images (HSIs) with 2D compressive measurements. The recovery of HSIs from these measurements is an ill-posed problem. This paper proposes a novel, to our knowledge, network architecture for this inverse problem, which consists of a multilevel residual network driven by patch-wise attention and a data pre-processing method. Specifically, we propose the patch attention module to adaptively generate heuristic clues by capturing uneven feature distribution and global correlations of different regions. By revisiting the data pre-processing stage, we present a complementary input method that effectively integrates the measurements and coded aperture. Extensive simulation experiments illustrate that the proposed network architecture outperforms state-of-the-art methods.Item Multi-spectral compressive snapshot imaging using RGB image sensors(The Optical Society of America, 2015-04-30) Rueda, Hoover; Lau, Daniel; Arce, Gonzalo R.; Hoover Rueda, Daniel Lau, and Gonzalo R. Arce; Rueda, Hoover; Arce, Gonzalo R.Compressive sensing is a powerful sensing and reconstruction framework for recovering high dimensional signals with only a handful of observations and for spectral imaging, compressive sensing offers a novel method of multispectral imaging. Specifically, the coded aperture snapshot spectral imager (CASSI) system has been demonstrated to produce multi-spectral data cubes color images from a single snapshot taken by a monochrome image sensor. In this paper, we expand the theoretical framework of CASSI to include the spectral sensitivity of the image sensor pixels to account for color and then investigate the impact on image quality using either a traditional color image sensor that spatially multiplexes red, green, and blue light filters or a novel Foveon image sensor which stacks red, green, and blue pixels on top of one another.Item QRnet: fast learning-based QR code image embedding(Multimedia Tools and Applications, 2022-02-16) Pena-Pena, Karelia; Lau, Daniel L.; Arce, Andrew J.; Arce, Gonzalo R.Quick Response (QR) codes usage in e-commerce is on the rise due to their versatility and ability to connect offline and online content, taking over almost every aspect of a business from posters to payments. Thus, many efforts have aimed at improving the visual quality of QR codes to be easily included in publicity designs in billboards and magazines. The most successful approaches, however, are slow since optimization algorithms are required for the generation of each beautified QR code, hindering its online customization. The aim of this paper is the fast generation of visually pleasant and robust QR codes. The proposed framework leverages state-of-the-art deep-learning algorithms to embed a color image into a baseline QR code in seconds while keeping a maximum probability of error during the decoding procedure. Halftoning techniques that exploit the human visual system (HVS) are used to smooth the embedding of the QR code structure in the final QR code image while reinforcing the decoding robustness. Compared to optimization-based methods, our framework provides similar qualitative results but is 3 orders of magnitude faster.Item Static coded aperture in robotic X-ray tomography systems(Optics Express, 2022-02-22) Mao, Tianyi; Ma, Xu; Cuadros, Angela P.; Dai, XiuBin; Wang, Zhiteng; Zhang, Xin; Zhu, Shujin; Zhu, Jianjian; Arce, Gonzalo R.Coded aperture X-ray computed tomography is a computational imaging technique capable of reconstructing inner structures of an object from a reduced set of X-ray projection measurements. Coded apertures are placed in front of the X-ray sources from different views and thus significantly reduce the radiation dose. This paper introduces coded aperture X-ray computed tomography for robotic X-ray systems which offer positioning flexibility. While single coded-aperture 3D tomography was recently introduced for standard trajectory CT scanning, it is shown that significant gains in imaging performance can be attained by simple modifications in the CT scanning trajectories enabled by emerging dual robotic CT systems. In particular, the subject is fixed on a plane and the CT system uniformly rotates around the r −axis which is misaligned with the coordinate axes. A single stationary coded aperture is placed on front of the robotic X-ray source above the plane and the corresponding X-ray projections are measured by a two-dimensional detector on the second arm of the robotic system. The compressive measurements with misalignment enable the reconstruction of high-resolution three-dimensional volumetric images from the low-resolution coded projections on the detector at a sub-sampling rate. An efficient algorithm is proposed to generate the rotation matrix with two basic sub-matrices and thus the forward model is formulated. The stationary coded aperture is designed based on the Pearson product-moment correlation coefficient analysis and the direct binary search algorithm is used to obtain the optimized coded aperture. Simulations using simulated datasets show significant gains in reconstruction performance compared to conventional coded aperture CT systems.Item Static coded illumination strategies for low-dose x-ray material decomposition(Applied Optics, 2022-01-20) Cuadros, Angela P.; Restrepo, Carlos M.; Noël, Peter; Arce, Gonzalo R.Static coded aperture x-ray tomography was introduced recently where a static illumination pattern is used to interrogate an object with a low radiation dose, from which an accurate 3D reconstruction of the object can be attained computationally. Rather than continuously switching the pattern of illumination with each view angle, as traditionally done, static code computed tomography (CT) places a single pattern for all views. The advantages are many, including the feasibility of practical implementation. This paper generalizes this powerful framework to develop single-scan dual-energy coded aperture spectral tomography that enables material characterization at a significantly reduced exposure level. Two sensing strategies are explored: rapid kV switching with a single-static block/unblock coded aperture, and coded apertures with non-uniform thickness. Both systems rely on coded illumination with a plurality of x-ray spectra created by kV switching or 3D coded apertures. The structured x-ray illumination is projected through the objects of interest and measured with standard x-ray energy integrating detectors. Then, based on the tensor representation of projection data, we develop an algorithm to estimate a full set of synthesized measurements that can be used with standard reconstruction algorithms to accurately recover the object in each energy channel. Simulation and experimental results demonstrate the effectiveness of the proposed cost-effective solution to attain material characterization in low-dose dual-energy CT.Item t-HGSP: Hypergraph Signal Processing Using t-Product Tensor Decompositions(IEEE Transactions on Signal and Information Processing over Networks, 2023-05-16) Pena-Pena, Karelia; Lau, Daniel L.; Arce, Gonzalo R.Graph signal processing (GSP) techniques are powerful tools that model complex relationships within large datasets, being now used in a myriad of applications in different areas including data science, communication networks, epidemiology, and sociology. Simple graphs can only model pairwise relationships among data which prevents their application in modeling networks with higher-order relationships. For this reason, some efforts have been made to generalize well-known graph signal processing techniques to more complex graphs such as hypergraphs, which allow capturing higher-order relationships among data. In this article, we provide a new hypergraph signal processing framework (t-HGSP) based on a novel tensor-tensor product algebra that has emerged as a powerful tool for preserving the intrinsic structures of tensors. The proposed framework allows the generalization of traditional GSP techniques while keeping the dimensionality characteristic of the complex systems represented by hypergraphs. To this end, the core elements of the t-HGSP framework are introduced, including the shifting operators and the hypergraph signal. The hypergraph Fourier space is also defined, followed by the concept of bandlimited signals and sampling. In our experiments, we demonstrate the benefits of our approach in applications such as clustering and denoising.