Summary of Swinsf: Image Reconstruction From Spatial-temporal Spike Streams, by Liangyan Jiang et al.
SwinSF: Image Reconstruction from Spatial-Temporal Spike Streams
by Liangyan Jiang, Chuang Zhu, Yanxu Chen
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces the Spike Camera, which tackles high-speed imaging challenges like motion blur by capturing photons at each pixel independently, creating binary spike streams rich in temporal information but challenging for image reconstruction. Current algorithms need improvement in utilizing this temporal detail and restoring the reconstructed image’s details. The authors propose Swin Spikeformer (SwinSF), a novel model for dynamic scene reconstruction from spike streams. SwinSF combines shifted window self-attention and proposed temporal spike attention, extracting comprehensive features that encapsulate spatial and temporal dynamics. The paper also introduces a new synthesized dataset matching the latest spike camera’s resolution, ensuring relevance to recent developments in spike camera imaging. Experimental results demonstrate SwinSF sets a new benchmark, achieving state-of-the-art performance across real-world and synthesized datasets with various resolutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Spike Camera is a special kind of camera that helps us take high-speed pictures without blurry motion. It works by capturing individual lights at each pixel separately, creating a special kind of data called spike streams. These spike streams are rich in information about what’s happening over time, but they’re hard to turn back into regular images. The authors want to improve the algorithms used to turn these spike streams into regular pictures, so they introduce a new model called Swin Spikeformer (SwinSF). This model is designed specifically for reconstructing dynamic scenes from spike streams and uses some clever techniques to capture both spatial and temporal information. To test their model, the authors created a new dataset that matches the latest technology in this field. Their results show that SwinSF performs better than previous methods on various datasets. |
Keywords
» Artificial intelligence » Attention » Self attention
