# Event-Stream Super Resolution using Sigma-Delta Neural Network

arXiv: https://arxiv.org/abs/2408.06968 · HTML: https://mlsanity.com/paper/2408.06968/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [End-to-End Learning of Representations for Asynchronous Event-Based Data](https://mlsanity.com/paper/1904.08245/) (2019)
- [Feedback Network for Image Super-Resolution](https://mlsanity.com/paper/1903.09814/) (2019)
- [Graph-Based Object Classification for Neuromorphic Vision Sensing](https://mlsanity.com/paper/1908.06648/) (2019)
- [EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning](https://mlsanity.com/paper/2003.07640/) (2020)
- [Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution](https://mlsanity.com/paper/2303.13767/) (2023)
- [Sigma Delta Quantized Networks](https://mlsanity.com/paper/1611.02024/) (2016)
- [Learning to Super-Resolve Blurry Images with Events](https://mlsanity.com/paper/2302.13766/) (2023)
- [Temporally Efficient Deep Learning with Spikes](https://mlsanity.com/paper/1706.04159/) (2017)
- [Deep Spiking Networks](https://mlsanity.com/paper/1602.08323/) (2016)
- [Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks](https://mlsanity.com/paper/1609.02053/) (2016)

## Built on by (most cited first, 1 of 1)

- [Ultralight Polarity-Split Neuromorphic SNN for Event-Stream Super-Resolution](https://mlsanity.com/paper/2508.03244/) (2025)

## Abstract (arXiv)

This study introduces a novel approach to enhance the spatial-temporal resolution of time-event pixels based on luminance changes captured by event cameras. These cameras present unique challenges due to their low resolution and the sparse, asynchronous nature of the data they collect. Current event super-resolution algorithms are not fully optimized for the distinct data structure produced by event cameras, resulting in inefficiencies in capturing the full dynamism and detail of visual scenes with improved computational complexity. To bridge this gap, our research proposes a method that integrates binary spikes with Sigma Delta Neural Networks (SDNNs), leveraging spatiotemporal constraint learning mechanism designed to simultaneously learn the spatial and temporal distributions of the event stream. The proposed network is evaluated using widely recognized benchmark datasets, including N-MNIST, CIFAR10-DVS, ASL-DVS, and Event-NFS. A comprehensive evaluation framework is employed, assessing both the accuracy, through root mean square error (RMSE), and the computational efficiency of our model. The findings demonstrate significant improvements over existing state-of-the-art methods, specifically, the proposed method outperforms state-of-the-art performance in computational efficiency, achieving a 17.04-fold improvement in event sparsity and a 32.28-fold increase in synaptic operation efficiency over traditional artificial neural networks, alongside a two-fold better performance over spiking neural networks.