EDmamba: A Simple yet Effective Event Denoising Method with State Space Model

• URL: http://arxiv.org/abs/2505.05391v2
• Date: Tue, 27 May 2025 06:07:57 GMT
• Title: EDmamba: A Simple yet Effective Event Denoising Method with State Space Model
• Authors: Ciyu Ruan, Zihang Gong, Ruishan Guo, Jingao Xu, Xinlei Chen,
• Abstract summary: Event cameras excel in high-speed vision due to their high temporal dynamic range, and low power consumption.<n>As dynamic vision sensors, their output is inherently noisy, making efficient denoising essential to preserve their ultra-low latency and real-time processing capabilities.<n>We propose a novel event denoising framework based on State Space Models (SSMs)
• Score: 20.776133942771768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event cameras excel in high-speed vision due to their high temporal resolution, high dynamic range, and low power consumption. However, as dynamic vision sensors, their output is inherently noisy, making efficient denoising essential to preserve their ultra-low latency and real-time processing capabilities. Existing event denoising methods struggle with a critical dilemma: computationally intensive approaches compromise the sensor's high-speed advantage, while lightweight methods often lack robustness across varying noise levels. To address this, we propose a novel event denoising framework based on State Space Models (SSMs). Our approach represents events as 4D event clouds and includes a Coarse Feature Extraction (CFE) module that extracts embedding features from both geometric and polarity-aware subspaces. The model is further composed of two essential components: A Spatial Mamba (S-SSM) that models local geometric structures and a Temporal Mamba (T-SSM) that captures global temporal dynamics, efficiently propagating spatiotemporal features across events. Experiments demonstrate that our method achieves state-of-the-art accuracy and efficiency, with 88.89K parameters, 0.0685s per 100K events inference time, and a 0.982 accuracy score, outperforming Transformer-based methods by 2.08% in denoising accuracy and 36X faster.

Related papers

• PRE-Mamba: A 4D State Space Model for Ultra-High-Frequent Event Camera Deraining [47.81253972389206]
  Event cameras excel in high temporal resolution and dynamic range but suffer from dense noise in rainy conditions.<n>We propose PRE-Mamba, a novel point-based camera framework for event deraining.
  arXiv  Detail & Related papers  (2025-05-08T14:52:45Z)
• Simultaneous Motion And Noise Estimation with Event Cameras [18.2247510082534]
  Event cameras are emerging vision sensors whose noise is challenging to characterize.<n>Existing denoising methods for event cameras are often designed in isolation.<n>We propose, to the best of our knowledge, the first method that simultaneously estimates motion in its various forms.
  arXiv  Detail & Related papers  (2025-04-05T02:47:40Z)
• FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion [63.87313550399871]
  Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability.<n>We propose Self-supervised Transfer (PST) and FrequencyDe-coupled Fusion module (FreDF)<n>PST establishes cross-modal knowledge transfer through latent space alignment with image foundation models.<n>FreDF explicitly decouples high-frequency edge features from low-frequency structural components, resolving modality-specific frequency mismatches.
  arXiv  Detail & Related papers  (2025-03-25T15:04:53Z)
• EgoEvGesture: Gesture Recognition Based on Egocentric Event Camera [17.61884467264023]
  We propose a novel network architecture specifically designed for event data processing.<n>We establish the first large-scale dataset for egocentric gesture recognition using event cameras.<n>Our method achieves 62.7% accuracy tested on unseen subjects with only 7M parameters, 3.1% higher than state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-03-16T09:08:02Z)
• One-Step Diffusion Model for Image Motion-Deblurring [85.76149042561507]
  We propose a one-step diffusion model for deblurring (OSDD), a novel framework that reduces the denoising process to a single step.<n>To tackle fidelity loss in diffusion models, we introduce an enhanced variational autoencoder (eVAE), which improves structural restoration.<n>Our method achieves strong performance on both full and no-reference metrics.
  arXiv  Detail & Related papers  (2025-03-09T09:39:57Z)
• Event-Based Tracking Any Point with Motion-Augmented Temporal Consistency [58.719310295870024]
  This paper presents an event-based framework for tracking any point.<n>It tackles the challenges posed by spatial sparsity and motion sensitivity in events.<n>It achieves 150% faster processing with competitive model parameters.
  arXiv  Detail & Related papers  (2024-12-02T09:13:29Z)
• LED: A Large-scale Real-world Paired Dataset for Event Camera Denoising [19.51468512911655]
  Event camera has significant advantages in capturing dynamic scene information while being prone to noise interference. We construct a new paired real-world event denoising dataset (LED), including 3K sequences with 18K seconds of high-resolution (1200*680) event streams. We propose a novel effective denoising framework(DED) using homogeneous dual events to generate the GT with better separating noise from the raw.
  arXiv  Detail & Related papers  (2024-05-30T06:02:35Z)
• Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement [85.66867277156089]
  We propose window-based event denoising, which simultaneously deals with a stack of events. In spatial domain, we choose maximum a posteriori (MAP) to discriminate real-world event and noise. Our algorithm can remove event noise effectively and efficiently and improve the performance of downstream tasks.
  arXiv  Detail & Related papers  (2024-02-14T15:56:42Z)
• The Missing U for Efficient Diffusion Models [3.712196074875643]
  Diffusion Probabilistic Models yield record-breaking performance in tasks such as image synthesis, video generation, and molecule design. Despite their capabilities, their efficiency, especially in the reverse process, remains a challenge due to slow convergence rates and high computational costs. We introduce an approach that leverages continuous dynamical systems to design a novel denoising network for diffusion models.
  arXiv  Detail & Related papers  (2023-10-31T00:12:14Z)
• Realistic Noise Synthesis with Diffusion Models [44.404059914652194]
  Deep denoising models require extensive real-world training data, which is challenging to acquire.<n>We propose a novel Realistic Noise Synthesis Diffusor (RNSD) method using diffusion models to address these challenges.
  arXiv  Detail & Related papers  (2023-05-23T12:56:01Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation [101.19290845597918]
  This paper presents a Motion Estimation (ME) module and an Event Denoising (ED) module jointly optimized in a mutually reinforced manner. Taking temporal correlation as guidance, ED module calculates the confidence that each event belongs to real activity events, and transmits it to ME module to update energy function of motion segmentation for noise suppression.
  arXiv  Detail & Related papers  (2022-03-22T13:40:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.