AIM 2020 Challenge on Video Temporal Super-Resolution

Son, Sanghyun; Lee, Jaerin; Nah, Seungjun; Timofte, Radu; Lee, Kyoung Mu

doi:10.48550/arxiv.2009.12987

Cited by 2 publications

(1 citation statement)

References 43 publications

(82 reference statements)

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“…Algorithm-based TSR (Software-only) approaches a straightforward solution in terms of system complexity, and these methods demonstrate good performance [26]. However, their ability to interpolate in time is limited since the deep learning models heavily rely on past examples and training.…”

Section: Introductionmentioning

confidence: 99%

Temporal Super-Resolution Using a Multi-Channel Illumination Source

Cohen,

Mendlovic,

Raviv

2024

Sensors

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While sensing in high temporal resolution is necessary for a wide range of applications, it is still limited nowadays due to the camera sampling rate. In this work, we try to increase the temporal resolution beyond the Nyquist frequency, which is limited by the sensor’s sampling rate. This work establishes a novel approach to temporal super-resolution that uses the object-reflecting properties from an active illumination source to go beyond this limit. Following theoretical derivation and the development of signal-processing-based algorithms, we demonstrate how to increase the detected temporal spectral range by a factor of six and possibly even more. Our method is supported by simulations and experiments, and we demonstrate (via application) how we use our method to dramatically improve the accuracy of object motion estimation. We share our simulation code on GitHub.

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Section: Introductionmentioning

confidence: 99%

Temporal Super-Resolution Using a Multi-Channel Illumination Source

Cohen,

Mendlovic,

Raviv

2024

Sensors

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CDFI: Compression-Driven Network Design for Frame Interpolation

Ding¹,

Liang²,

Zhu³

et al. 2021

Preprint

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DNN-based frame interpolation-that generates the intermediate frames given two consecutive frames-typically relies on heavy model architectures with a huge number of features, preventing them from being deployed on systems with limited resources, e.g., mobile devices. We propose a compression-driven network design for frame interpolation (CDFI), that leverages model pruning through sparsityinducing optimization to significantly reduce the model size while achieving superior performance. Concretely, we first compress the recently proposed AdaCoF model and show that a 10× compressed AdaCoF performs similarly as its original counterpart; then we further improve this compressed model by introducing a multi-resolution warping module, which boosts visual consistencies with multi-level details. As a consequence, we achieve a significant performance gain with only a quarter in size compared with the original AdaCoF. Moreover, our model performs favorably against other state-of-the-arts in a broad range of datasets. Finally, the proposed compression-driven framework is generic and can be easily transferred to other DNNbased frame interpolation algorithm. Our source code is available at https://github.com/tding1/CDFI. * Equal contribution. This work was done when Tianyu Ding was an intern at Applied Sciences Group, Microsoft.† Corresponding author.Recently, a large number of researches have been conducted in this area, especially those based on deep neural networks (DNN) for their promising results in motion esti-

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AIM 2020 Challenge on Video Temporal Super-Resolution

Cited by 2 publications

References 43 publications

Temporal Super-Resolution Using a Multi-Channel Illumination Source

Temporal Super-Resolution Using a Multi-Channel Illumination Source

CDFI: Compression-Driven Network Design for Frame Interpolation

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