# A Retina-inspired Sampling Method for Visual Texture Reconstruction

arXiv: https://arxiv.org/abs/1907.08769 · HTML: https://mlsanity.com/paper/1907.08769/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2019.

## Built on by (most cited first, 30 of 34)

- [Event-based Video Reconstruction via Potential-assisted Spiking Neural Network](https://mlsanity.com/paper/2201.10943/) (2022)
- [Optical Flow Estimation for Spiking Camera](https://mlsanity.com/paper/2110.03916/) (2021)
- [Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition](https://mlsanity.com/paper/2306.11546/) (2023)
- [Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer](https://mlsanity.com/paper/2208.12527/) (2022)
- [SpikeCV: Open a Continuous Computer Vision Era](https://mlsanity.com/paper/2303.11684/) (2023)
- [SpikeNeRF: Learning Neural Radiance Fields from Continuous Spike Stream](https://mlsanity.com/paper/2403.11222/) (2024)
- [SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams](https://mlsanity.com/paper/2403.09486/) (2024)
- [SpikeGS: 3D Gaussian Splatting from Spike Streams with High-Speed Camera Motion](https://mlsanity.com/paper/2407.10062/) (2024)
- [Unveiling the Potential of Spike Streams for Foreground Occlusion Removal from Densely Continuous Views](https://mlsanity.com/paper/2307.00821/) (2023)
- [Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera](https://mlsanity.com/paper/2307.06003/) (2023)
- [Recurrent Spike-based Image Restoration under General Illumination](https://mlsanity.com/paper/2308.03018/) (2023)
- [SpikeGS: Reconstruct 3D scene via fast-moving bio-inspired sensors](https://mlsanity.com/paper/2407.03771/) (2024)
- [SOTA: Spike-Navigated Optimal TrAnsport Saliency Region Detection in Composite-bias Videos](https://mlsanity.com/paper/2505.00394/) (2025)
- [Finding Visual Saliency in Continuous Spike Stream](https://mlsanity.com/paper/2403.06233/) (2024)
- [USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting](https://mlsanity.com/paper/2411.10504/) (2024)
- [Uncertainty Guided Depth Fusion for Spike Camera](https://mlsanity.com/paper/2208.12653/) (2022)
- [SpikeCodec: An End-to-end Learned Compression Framework for Spiking Camera](https://mlsanity.com/paper/2306.14108/) (2023)
- [SCSim: A Realistic Spike Cameras Simulator](https://mlsanity.com/paper/2405.16790/) (2024)
- [SpikeMM: Flexi-Magnification of High-Speed Micro-Motions](https://mlsanity.com/paper/2406.00383/) (2024)
- [High-speed and High-quality Vision Reconstruction of Spike Camera with Spike Stability Theorem](https://mlsanity.com/paper/2412.11639/) (2024)
- [Rethinking High-speed Image Reconstruction Framework with Spike Camera](https://mlsanity.com/paper/2501.04477/) (2025)
- [SpikeDerain: Unveiling Clear Videos from Rainy Sequences Using Color Spike Streams](https://mlsanity.com/paper/2503.20315/) (2025)
- [Cricket: A Self-Powered Chirping Pixel](https://mlsanity.com/paper/2505.02246/) (2025)
- [SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams](https://mlsanity.com/paper/2505.19487/) (2025)
- [A Robust Visual Sampling Model Inspired by Receptive Field](https://mlsanity.com/paper/2201.01030/) (2022)
- [Spike Stream Denoising via Spike Camera Simulation](https://mlsanity.com/paper/2304.03129/) (2023)
- [Learning to Robustly Reconstruct Low-light Dynamic Scenes from Spike Streams](https://mlsanity.com/paper/2401.10461/) (2024)
- [SwinSF: Image Reconstruction from Spatial-Temporal Spike Streams](https://mlsanity.com/paper/2407.15708/) (2024)
- [SpikeGen: Decoupled "Rods and Cones" Visual Representation Processing with Latent Generative Framework](https://mlsanity.com/paper/2505.18049/) (2025)
- [SPACT18: Spiking Human Action Recognition Benchmark Dataset with Complementary RGB and Thermal Modalities](https://mlsanity.com/paper/2507.16151/) (2025)

## Abstract (arXiv)

Conventional frame-based camera is not able to meet the demand of rapid reaction for real-time applications, while the emerging dynamic vision sensor (DVS) can realize high speed capturing for moving objects. However, to achieve visual texture reconstruction, DVS need extra information apart from the output spikes. This paper introduces a fovea-like sampling method inspired by the neuron signal processing in retina, which aims at visual texture reconstruction only taking advantage of the properties of spikes. In the proposed method, the pixels independently respond to the luminance changes with temporal asynchronous spikes. Analyzing the arrivals of spikes makes it possible to restore the luminance information, enabling reconstructing the natural scene for visualization. Three decoding methods of spike stream for texture reconstruction are proposed for high-speed motion and stationary scenes. Compared to conventional frame-based camera and DVS, our model can achieve better image quality and higher flexibility, which is capable of changing the way that demanding machine vision applications are built.