Quantifying Noise of Dynamic Vision Sensor

• URL: http://arxiv.org/abs/2404.01948v1
• Date: Tue, 2 Apr 2024 13:43:08 GMT
• Title: Quantifying Noise of Dynamic Vision Sensor
• Authors: Evgeny V. Votyakov, Alessandro Artusi,
• Abstract summary: Dynamic visual sensors (DVS) are characterised by a large amount of background activity (BA) noise. It is difficult to distinguish between noise and the cleaned sensor signals using standard image processing techniques. A new technique is presented to characterise BA noise derived from the Detrended Fluctuation Analysis (DFA)
• Score: 49.665407116447454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dynamic visual sensors (DVS) are characterized by a large amount of background activity (BA) noise, which it is mixed with the original (cleaned) sensor signal. The dynamic nature of the signal and the absence in practical application of the ground truth, it clearly makes difficult to distinguish between noise and the cleaned sensor signals using standard image processing techniques. In this letter, a new technique is presented to characterise BA noise derived from the Detrended Fluctuation Analysis (DFA). The proposed technique can be used to address an existing DVS issues, which is how to quantitatively characterised noise and signal without ground truth, and how to derive an optimal denoising filter parameters. The solution of the latter problem is demonstrated for the popular real moving-car dataset.

Related papers

• Unsupervised Denoising for Signal-Dependent and Row-Correlated Imaging Noise [54.0185721303932]
  We present the first fully unsupervised deep learning-based denoiser capable of handling imaging noise that is row-correlated. Our approach uses a Variational Autoencoder with a specially designed autoregressive decoder. Our method does not require a pre-trained noise model and can be trained from scratch using unpaired noisy data.
  arXiv  Detail & Related papers  (2023-10-11T20:48:20Z)
• Towards General Low-Light Raw Noise Synthesis and Modeling [37.87312467017369]
  We introduce a new perspective to synthesize the signal-independent noise by a generative model. Specifically, we synthesize the signal-dependent and signal-independent noise in a physics- and learning-based manner. In this way, our method can be considered as a general model, that is, it can simultaneously learn different noise characteristics for different ISO levels.
  arXiv  Detail & Related papers  (2023-07-31T09:10:10Z)
• DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection [0.0]
  RCNN-based pedestrian detectors use rectangle regions to extract instance features. The number of severely overlapping objects and the number of slightly overlapping objects are unbalanced. An iterable dynamic instance noise filter (DINF) is proposed for the RCNN-based pedestrian detectors to improve the signal-noise ratio of the instance feature.
  arXiv  Detail & Related papers  (2023-01-13T14:12:36Z)
• CFNet: Conditional Filter Learning with Dynamic Noise Estimation for Real Image Denoising [37.29552796977652]
  This paper considers real noise approximated by heteroscedastic Gaussian/Poisson Gaussian distributions with in-camera signal processing pipelines. We propose a novel conditional filter in which the optimal kernels for different feature positions can be adaptively inferred by local features from the image and the noise map. Also, we bring the thought that alternatively performs noise estimation and non-blind denoising into CNN structure, which continuously updates noise prior to guide the iterative feature denoising.
  arXiv  Detail & Related papers  (2022-11-26T14:28:54Z)
• Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising [137.5080784570804]
  Video denoising aims at removing noise from videos to recover clean ones. Some existing works show that optical flow can help the denoising by exploiting the additional spatial-temporal clues from nearby frames. We propose a new multi-scale refined optical flow-guided video denoising method, which is more robust to different noise levels.
  arXiv  Detail & Related papers  (2022-08-25T00:09:18Z)
• SAR Despeckling using a Denoising Diffusion Probabilistic Model [52.25981472415249]
  The presence of speckle degrades the image quality and adversely affects the performance of SAR image understanding applications. We introduce SAR-DDPM, a denoising diffusion probabilistic model for SAR despeckling. The proposed method achieves significant improvements in both quantitative and qualitative results over the state-of-the-art despeckling methods.
  arXiv  Detail & Related papers  (2022-06-09T14:00:26Z)
• FINO: Flow-based Joint Image and Noise Model [23.9749061109964]
  Flow-based joint Image and NOise model (FINO) We propose a novel Flow-based joint Image and NOise model (FINO) that distinctly decouples the image and noise in the latent space and losslessly reconstructs them via a series of invertible transformations.
  arXiv  Detail & Related papers  (2021-11-11T02:51:54Z)
• Rethinking Noise Synthesis and Modeling in Raw Denoising [75.55136662685341]
  We introduce a new perspective to synthesize noise by directly sampling from the sensor's real noise. It inherently generates accurate raw image noise for different camera sensors.
  arXiv  Detail & Related papers  (2021-10-10T10:45:24Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

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.