Deep Equilibrium Models for Video Snapshot Compressive Imaging

Zhao, Ying‐Zheng; Yuan, Xin

doi:10.48550/arxiv.2201.06931

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…We present a quantitative comparison to compare the quality performances of the following VCS algorithms: GAP-TV [44], DeSCI [73], PnP-FFDNet [74], Pnp-FastDVDNet [88], GAP-FastDVDNet(online) [85], DE-RNN [86], DE-GAP-FFDnet [86], E2E-CNN [48], BIR-NAT [75], MetaSCI [83], RevSCI [82], DeepUnfold-VCS [51], GAP-Unet-S12 [76], ELP-Unfolding [84].…”

Section: Benchmark Resultsmentioning

confidence: 99%

“…The authors present an ADMM optimization and deep neural network to improve the output quality. Finally, a deep equilibrium-based model is proposed in [86] that combines data-driven regularization and stable convergence to deal with the problems of memory requirement and unstable reconstruction in some exiting approaches.…”

Section: Video Snapshot Compressive Imagingmentioning

confidence: 99%

See 1 more Smart Citation

An Overview on Deep Learning Techniques for Video Compressive Sensing

et al. 2022

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The use of compressive sensing in several applications has allowed to capture impressive results, especially in various applications such as image and video processing and it has become a promising direction of scientific research. It provides extensive application value in optimizing video surveillance networks. In this paper, we introduce recent state-of-the-art video compressive sensing methods based on neural networks and categorize them into different categories. We compare these approaches by analyzing the networks architectures. Then, we present their pros and cons. The general conclusion of the paper identify open research challenges and point out future research directions. The goal of this paper is to overview the current approaches in image and video compressive sensing and demonstrate their powerful impact in computer vision when using well designed compressive sensing algorithms.

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Section: Benchmark Resultsmentioning

confidence: 99%

Section: Video Snapshot Compressive Imagingmentioning

confidence: 99%

An Overview on Deep Learning Techniques for Video Compressive Sensing

et al. 2022

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“…In addition to spectral SCI reconstruction as shown in this work, we do believe our network can be used in medical images [ 59 ], image compression [ 60 ], temporal compressive coherent diffraction imaging [ 61 ], and video compressive sensing [ 62 , 63 , 64 , 65 , 66 ].…”

Section: Discussionmentioning

confidence: 99%

Hybrid Multi-Dimensional Attention U-Net for Hyperspectral Snapshot Compressive Imaging Reconstruction

Zheng

Zhu

Chen

2023

Entropy

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In order to capture the spatial-spectral (x,y,λ) information of the scene, various techniques have been proposed. Different from the widely used scanning-based methods, spectral snapshot compressive imaging (SCI) utilizes the idea of compressive sensing to compressively capture the 3D spatial-spectral data-cube in a single-shot 2D measurement and thus it is efficient, enjoying the advantages of high-speed and low bandwidth. However, the reconstruction process, , to retrieve the 3D cube from the 2D measurement, is an ill-posed problem and it is challenging to reconstruct high quality images. Previous works usually use 2D convolutions and preliminary attention to address this challenge. However, these networks and attention do not exactly extract spectral features. On the other hand, 3D convolutions can extract more features in a 3D cube, but increase computational cost significantly. To balance this trade-off, in this paper, we propose a hybrid multi-dimensional attention U-Net (HMDAU-Net) to reconstruct hyperspectral images from the 2D measurement in an end-to-end manner. HMDAU-Net integrates 3D and 2D convolutions in an encoder–decoder structure to fully utilize the abundant spectral information of hyperspectral images with a trade-off between performance and computational cost. Furthermore, attention gates are employed to highlight salient features and suppress the noise carried by the skip connections. Our proposed HMDAU-Net achieves superior performance over previous state-of-the-art reconstruction algorithms.

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“…nonlinear optics [16], meta-hologram [17]) are adopted in image cryptography. Compressive sensing [18] that is primarily used for data shrinking, also contributes to low-cost security enhancements [19]. Target to neuromorphic imaging, a recent proposal bridged traditional chaotic schemes with polarity flipping [3].…”

Section: Related Workmentioning

confidence: 99%

Event encryption: rethinking privacy exposure for neuromorphic imaging

Zhang,

Zhu,

Lam

2024

Neuromorph. Comput. Eng.

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Bio-inspired neuromorphic cameras sense illumination changes on a per-pixel basis and generate spatiotemporal streaming events within microseconds in response, offering visual information with high temporal resolution over a high dynamic range. Such devices often serve in surveillance systems due to their applicability and robustness in environments with high dynamics and harsh lighting, where they can still supply clearer recordings than traditional imaging. In other words, when it comes to privacy-relevant cases, neuromorphic cameras also expose more sensitive data and pose serious security threats. Therefore, asynchronous event streams necessitate careful encryption before transmission and usage. This work discusses several potential attack scenarios and approaches event encryption from the perspective of neuromorphic noise removal, in which we inversely introduce well-crafted noise into raw events until they are obfuscated. Our evaluations show that the encrypted events can effectively protect information from attacks of low-level visual reconstruction and high-level neuromorphic reasoning, and thus feature dependable privacy-preserving competence. The proposed solution gives impetus to the security of event data and paves the way to a highly encrypted technique for privacy-protective neuromorphic imaging.

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Deep Equilibrium Models for Video Snapshot Compressive Imaging

Cited by 4 publications

References 25 publications

An Overview on Deep Learning Techniques for Video Compressive Sensing

An Overview on Deep Learning Techniques for Video Compressive Sensing

Hybrid Multi-Dimensional Attention U-Net for Hyperspectral Snapshot Compressive Imaging Reconstruction

Event encryption: rethinking privacy exposure for neuromorphic imaging

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