Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables

Xu, Shaojie; Zeng, Sihan; Romberg, Justin

doi:10.1109/icassp.2019.8683641

Cited by 19 publications

(17 citation statements)

References 18 publications

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“…Later efforts focused on solving problems with specific structures, e.g., NLR (Nonlocal Low-Rank Regularization) [67] and SGSR (Structural Group Sparse Representation) [48]. Nowadays, combining compressed sensing with the popular deep learning, the deep-CS [68][69][70][71][72][73][74][75][76][77] becomes the focus of current research. Owing to the complex structures of trees, it is challenging to select an algorithm that satisfies both criterions of low computation time and high accuracy.…”

Section: Selection Of Reconstruction Algorithmmentioning

confidence: 99%

Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing

Yun

Cao

et al. 2020

Forests

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The terrestrial laser scanner (TLS) has been widely used in forest inventories. However, with increasing precision of TLS, storing and transmitting tree point clouds become more challenging. In this paper, a novel compressed sensing (CS) scheme for broad-leaved tree point clouds is proposed by analyzing and comparing different sparse bases, observation matrices, and reconstruction algorithms. Our scheme starts by eliminating outliers and simplifying point clouds with statistical filtering and voxel filtering. The scheme then applies Haar sparse basis to thin the coordinate data based on the characteristics of the broad-leaved tree point clouds. An observation procedure down-samples the point clouds with the partial Fourier matrix. The regularized orthogonal matching pursuit algorithm (ROMP) finally reconstructs the original point clouds. The experimental results illustrate that the proposed scheme can preserve morphological attributes of the broad-leaved tree within a range of relative error: 0.0010%–3.3937%, and robustly extend to plot-level within a range of mean square error (MSE): 0.0063–0.2245.

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Section: Selection Of Reconstruction Algorithmmentioning

confidence: 99%

Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing

Yun

Cao

et al. 2020

Forests

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“…Practically, to a certain extent, the real-time images can reflect the influence of atmosphere turbulence. To test the effectiveness of the image saving, we determined the parameters in a reconstructed image from four different algorithms for comparison: Irls [ 17 ], ISTA-Net [ 22 ] and Ols [ 20 , 21 ] and FCSR [ 23 ]. Images without noise but still with distortion of atmosphere turbulence are used for this controlled experiment.…”

Section: Image Storagementioning

confidence: 99%

“…Practically, it is a mature technology for this problem by use of traditional optimization or deep-learning methods [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Laser Communication Beacon Light Compressed Sensing

Wang

Gao

Sheng

2020

Sensors

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The Compressed Sensing (CS) camera can compress images in real time without consuming computing resources. Applying CS theory in the Laser Communication (LC) system can minimize the assumed transmission bandwidth (normally from a satellite to a ground station) and minimize the storage costs of beacon light-spot images; this can save more than ten times the typical bandwidth or storage space. However, the CS compressive process affects the light-spot tracking and key parameters in the images. In this study, we quantitatively explored the feasibility of the CS technique to capture light-spots in LC systems. We redesigned the measurement matrix to adapt to the requirement of light-tracking. We established a succinct structured deep network, the Compressed Sensing Denoising Center Net (CSD-Center Net) for denoising tracking computation from compressed image information. A series of simulations was made to test the performance of information preservation in beacon light spot image storage. With the consideration of CS ratio and application scenarios, coupled with CSD-Center Net and standard centroid, CS can achieve the tracking function well. The information preserved in compressed information correlates with the CS ratio; higher CS ratio can preserve more details. In fact, when the data rate is up than 10%, the accuracy could meet the requirements what we need in most application scenarios.

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“…In this paper, we consider generative model based compressive sensing (GMCS) [12][13][14][15][16], as opposed to sparsityprior-based CS. Most commonly used generative models are variational auto-encoders (VAE) [17] or generative adversarial networks (GAN) [18].…”

Section: Introductionmentioning

confidence: 99%

Sparsity Driven Latent Space Sampling for Generative Prior Based Compressive Sensing

Killedar

Pokala

Seelamantula

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We address the problem of recovering signals from compressed measurements based on generative priors. Recently, generative-model based compressive sensing (GMCS) methods have shown superior performance over traditional compressive sensing (CS) techniques in recovering signals from fewer measurements. However, it is possible to further improve the performance of GMCS by introducing controlled sparsity in the latent-space. We propose a proximal metalearning (PML) algorithm to enforce sparsity in the latentspace while training the generator. Enforcing sparsity naturally leads to a union-of-submanifolds model in the solution space. The overall framework is named as sparsity driven latent space sampling (SDLSS). In addition, we derive the sample complexity bounds for the proposed model. Furthermore, we demonstrate the efficacy of the proposed framework over the state-of-the-art techniques with application to CS on standard datasets such as MNIST and CIFAR-10. In particular, we evaluate the performance of the proposed method as a function of the number of measurements and sparsity factor in the latent space using standard objective measures. Our findings show that the sparsity driven latent space sampling approach improves the accuracy and aids in faster recovery of the signal in GMCS.

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Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables

Cited by 19 publications

References 18 publications

Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing

Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing

Feasibility of Laser Communication Beacon Light Compressed Sensing

Sparsity Driven Latent Space Sampling for Generative Prior Based Compressive Sensing

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