SpikeMM: Flexi-Magnification of High-Speed Micro-Motions
Not on the map. Why?
Abstract (arXiv)
The amplification of high-speed micro-motions holds significant promise, with applications spanning fault detection in fast-paced industrial environments to refining precision in medical procedures. However, conventional motion magnification algorithms often encounter challenges in high-speed scenarios due to low sampling rates or motion blur. In recent years, spike cameras have emerged as a superior alternative for visual tasks in such environments, owing to their unique capability to capture temporal and spatial frequency domains with exceptional fidelity. Unlike conventional cameras, which operate at fixed, low frequencies, spike cameras emulate the functionality of the retina, asynchronously capturing photon changes at each pixel position using spike streams. This innovative approach comprehensively records temporal and spatial visual information, rendering it particularly suitable for magnifying high-speed micro-motions.This paper introduces SpikeMM, a pioneering spike-based algorithm tailored specifically for high-speed motion magnification. SpikeMM integrates multi-level information extraction, spatial upsampling, and motion magnification modules, offering a self-supervised approach adaptable to a wide range of scenarios. Notably, SpikeMM facilitates seamless integration with high-performance super-resolution and motion magnification algorithms. We substantiate the efficacy of SpikeMM through rigorous validation using scenes captured by spike cameras, showcasing its capacity to magnify motions in real-world high-frequency settings.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 57 references that are in the map. First posted on arXiv 1 Jun 2024.
Builds on
20 of 57 references.
References · 20
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,415
- 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
- 2021KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPscited by 673
- 2023WIRE: Wavelet Implicit Neural Representationscited by 220
- 2019High-Quality Self-Supervised Deep Image Denoisingcited by 161
- 2021Beyond Periodicity: Towards a Unifying Framework for Activations in Coordinate-MLPscited by 135
- 2023Deep Learning on Implicit Neural Representations of Shapescited by 63
- 2018Learning-based Video Motion Magnificationcited by 43
- 2019A Retina-inspired Sampling Method for Visual Texture Reconstructioncited by 34
- 2021Optical Flow Estimation for Spiking Cameracited by 29
- 20221000x Faster Camera and Machine Vision with Ordinary Devicescited by 25
8 more
- 2017Video Acceleration Magnificationcited by 16
- 2023Regularize implicit neural representation by itselfcited by 13
- 2023Self-Supervised Motion Magnification by Backpropagating Through Optical Flowcited by 10
- 2022Lagrangian Motion Magnification with Double Sparse Optical Flow Decompositioncited by 7
- 2023SpikeCV: Open a Continuous Computer Vision Eracited by 7
- 2020Towards the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikescited by 6
- 2023Unveiling the Potential of Spike Streams for Foreground Occlusion Removal from Densely Continuous Viewscited by 5
- 2018Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networkscited by 3
Built on by
Citing papers per year: 2024: 1.
1 paper.