Real-time Motion Detection in Extremely Subsampled Compressive Sensing Video

Ralašić, Ivan; Seršić, Damir

doi:10.1109/icsipa45851.2019.8977759

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Learning based methods for the CS image reconstruction present an efficient alternative to the traditional approach. Average per-image reconstruction time for a set of images with size 512 × 512 using traditional l 1 reconstruction method from the Sparse Modelling Software (SPAMS, 2010) optimization toolbox and a block-based approach with a subsampling rate of r = 0.04 is around 0.6 s, while the learning based method reduces the reconstruction time to around 0.025 s. An example of a real-world application of the learning-based approach is (Ralašić and Seršić, 2019), where the authors propose a real-time motion detection system in CS video which operates at extremely low measurement rates.…”

Section: Discussionmentioning

confidence: 99%

Perceptual Autoencoder for Compressive Sensing Image Reconstruction

Ralašić¹,

Seršić²,

Šegvić³

2020

Informatica

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This paper presents a non-iterative deep learning approach to compressive sensing (CS) image reconstruction using a convolutional autoencoder and a residual learning network. An efficient measurement design is proposed in order to enable training of the compressive sensing models on normalized and mean-centred measurements, along with a practical network initialization method based on principal component analysis (PCA). Finally, perceptual residual learning is proposed in order to obtain semantically informative image reconstructions along with high pixel-wise reconstruction accuracy at low measurement rates.

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Section: Discussionmentioning

confidence: 99%

Perceptual Autoencoder for Compressive Sensing Image Reconstruction

Ralašić¹,

Seršić²,

Šegvić³

2020

Informatica

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“…Compressive sensing is a signal processing framework which consists of a linear measurement and a nonlinear reconstruction process which is based on sparse optimization [15][16][17]. Research on various applications of the CS framework [18][19][20][21][22] has been very active in the recent years in different scientific areas.…”

Section: Compressive Sensingmentioning

confidence: 99%

Overcoming spatio-angular trade-off in light field acquisition using compressive sensing

et al. 2020

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In contrast to conventional cameras which capture a 2D projection of a 3D scene by integrating the angular domain, light field cameras preserve the angular information of individual light rays by capturing a 4D light field of a scene. On the one hand, light field photography enables powerful post-capture capabilities such as refocusing, virtual aperture, depth sensing and perspective shift. On the other hand, it has several drawbacks, namely, high-dimensionality of the captured light fields and a fundamental trade-off between spatial and angular resolution in the camera design. In this paper, we propose a compressive sensing approach to light field acquisition from a sub-Nyquist number of samples. Using an off-the-shelf measurement setup consisting of a digital projector and a Lytro Illum light field camera, we demonstrate the efficiency of the compressive sensing approach by improving the spatial resolution of the acquired light field. This paper presents a proof of concept with a simplified 3D scene as the scene of interest. Results obtained by the proposed method show significant improvement in the spatial resolution of the light field as well as preserved post-capture capabilities.

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Real-time Motion Detection in Extremely Subsampled Compressive Sensing Video

Cited by 2 publications

References 24 publications

Perceptual Autoencoder for Compressive Sensing Image Reconstruction

Perceptual Autoencoder for Compressive Sensing Image Reconstruction

Overcoming spatio-angular trade-off in light field acquisition using compressive sensing

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