# Within-Camera Multilayer Perceptron DVS Denoising

arXiv: https://arxiv.org/abs/2304.07543 · HTML: https://mlsanity.com/paper/2304.07543/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 15 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors](https://mlsanity.com/paper/2006.10159/) (2020)
- [Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras](https://mlsanity.com/paper/2003.08282/) (2020)
- [Neuromorphic Camera Denoising using Graph Neural Network-driven Transformers](https://mlsanity.com/paper/2112.09685/) (2021)
- [Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification](https://mlsanity.com/paper/1905.07419/) (2019)
- [Hardware architecture for high throughput event visual data filtering with matrix of IIR filters algorithm](https://mlsanity.com/paper/2207.00860/) (2022)
- [A 51.3 TOPS/W, 134.4 GOPS In-memory Binary Image Filtering in 65nm CMOS](https://mlsanity.com/paper/2107.11723/) (2021)

## Built on by (most cited first, 8 of 8)

- [From Sim-to-Real: Toward General Event-based Low-light Frame Interpolation with Per-scene Optimization](https://mlsanity.com/paper/2406.08090/) (2024)
- [Event-Based Motion Magnification](https://mlsanity.com/paper/2402.11957/) (2024)
- [Event-based vision on FPGAs -- a survey](https://mlsanity.com/paper/2407.08356/) (2024)
- [Learning to Remove Lens Flare in Event Camera](https://mlsanity.com/paper/2512.09016/) (2025)
- [Learning from Noise: Enhancing DNNs for Event-Based Vision through Controlled Noise Injection](https://mlsanity.com/paper/2506.03918/) (2025)
- [A Label-Free and Non-Monotonic Metric for Evaluating Denoising in Event Cameras](https://mlsanity.com/paper/2406.08909/) (2024)
- [High Throughput Event Filtering: The Interpolation-based DIF Algorithm Hardware Architecture](https://mlsanity.com/paper/2506.05825/) (2025)
- [Real-time Unsupervised Object Discovery from Asynchronous Event Streams](https://mlsanity.com/paper/2608.26644/) (2026)

## Abstract (arXiv)

In-camera event denoising reduces the data rate of event cameras by filtering out noise at the source. A lightweight multilayer perceptron denoising filter (MLPF) provides state-of-the-art low-cost denoising accuracy. It processes a small neighborhood of pixels from the timestamp image around each event to discriminate signal and noise events. This paper proposes two digital logic implementations of the MLPF denoiser and quantifies their resource cost, power, and latency. The hardware MLPF quantizes the weights and hidden unit activations to 4 bits and has about 1k weights with about 40% sparsity. The Area-Under-Curve Receiver Operating Characteristic accuracy is nearly indistinguishable from that of the floating point network. The FPGA MLPF processes each event in 10 clock cycles. In FPGA, it uses 3.5k flip flops and 11.5k LUTs. Our ASIC implementation in 65nm digital technology for a 346x260 pixel camera occupies an area of 4.3mm^2 and consumes 4nJ of energy per event at event rates up to 25MHz. The MLPF can be easily integrated into an event camera using an FPGA or as an ASIC directly on the camera chip or in the same package. This denoising could dramatically reduce the energy consumed by the communication and host processor and open new areas of always-on event camera application under scavenged and battery power. Code: https://github.com/SensorsINI/dnd_hls