A Linear Comb Filter for Event Flicker Removal

Wang, Ziwei; Yuan, Dingran; Ng, Yonhon; Mahony, Robert

doi:10.1109/icra46639.2022.9812003

Cited by 10 publications

(7 citation statements)

References 28 publications

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“…While batch methods have achieved high accuracy, they incur additional latency depending on the time interval of the batch (e.g., 50ms). Asynchronous methods, if implemented on appropriate hardware and software, have the potential to run on a timescale closer to that of events < 1ms, which has recently shown great potential in high-speed optical communication [34], tracking [35] and high-frequency signal processing [36], [37], [38]. A further distinction may be made between pure event reconstruction methods and hybrid event-frame methods that use a mix of (registered) events and image frames.…”

Section: Related Workmentioning

confidence: 99%

An Asynchronous Kalman Filter for Hybrid Event Cameras

Wang

Scheerlinck

et al. 2021

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

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Event cameras are ideally suited to capture High Dynamic Range (HDR) visual information without blur but provide poor imaging capability for static or slowly varying scenes. Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on HDR or quickly changing scenes. In this paper, we present an asynchronous linear filter architecture, fusing event and frame camera data, for HDR video reconstruction and spatial convolution that exploits the advantages of both sensor modalities. The key idea is the introduction of a state that directly encodes the integrated or convolved image information and that is updated asynchronously as each event or each frame arrives from the camera. The state can be read-off as-often-as and whenever required to feed into subsequent vision modules for real-time robotic systems. Our experimental results are evaluated on both publicly available datasets with challenging lighting conditions and fast motions, along with a new dataset with HDR reference that we provide. The proposed AKF pipeline outperforms other state-of-the-art methods in both absolute intensity error (69.4% reduction) and image similarity indexes (average 35.5% improvement). We also demonstrate the integration of image convolution with linear spatial kernels Gaussian, Sobel, and Laplacian as an application of our architecture.

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Section: Related Workmentioning

confidence: 99%

An Asynchronous Kalman Filter for Hybrid Event Cameras

Wang

Scheerlinck

et al. 2021

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

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“…Ref. [28] develops a comb filter in a continuous-time approach, but in this case the frequency is assumed to be known, whereas our goal is to detect the frequency in the first place.…”

Section: Related Workmentioning

confidence: 99%

“…3.4). Our event time based method is in contrast to the filtering performed in [28] which operates in continous time. Since the filter does not make use of any time stamps its output is invariant to the (wall clock) time scale, i.e.…”

Section: Reconstruction By Digital Filteringmentioning

confidence: 99%

“…The present work addresses a much more basic task that has received surprisingly little attention: how to detect the frequency of periodically varying signals on a per-pixel level. Often periodic flickering is considered a nuisance signal to be filtered out [28], however there is commercial interest for frequency detection in the context of vibration analysis. In fact Prophesee's Metavision Source Development Kit (SDK) contains a vibration analysis module to which we will compare (Fig.…”

Section: Introductionmentioning

confidence: 99%

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Frequency Cam: Imaging Periodic Signals in Real-Time

Pfrommer¹

2022

Preprint

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Due to their high temporal resolution and large dynamic range event cameras are uniquely suited for the analysis of time-periodic signals in an image. In this work we present an efficient and fully asynchronous event camera algorithm for detecting the fundamental frequency at which image pixels flicker. The algorithm employs a second-order digital infinite impulse response (IIR) filter to perform an approximate per-pixel brightness reconstruction and is more robust to high-frequency noise than the baseline method we compare to. We further demonstrate that using the falling edge of the signal leads to more accurate period estimates than the rising edge, and that for certain signals interpolating the zero-level crossings can further increase accuracy. Our experiments find that the outstanding capabilities of the camera in detecting frequencies up to 64kHz for a single pixel do not carry over to full sensor imaging as readout bandwidth limitations become a serious obstacle. This suggests that a hardware implementation closer to the sensor will allow for greatly improved frequency imaging. We discuss the important design parameters for fullsensor frequency imaging and present Frequency Cam, an open-source implementation as a ROS node that can run on a single core of a laptop CPU at more than 50 million events/sec. It produces results that are qualitatively very similar to those obtained from the closed source vibration analysis module in Prophesee's Metavision Toolkit. The code for Frequency Cam and a demonstration video can be found at https://github.com/berndpfrommer/ frequency_cam.

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“…Compared to existing methods, their STCF algorithm filters out events that lack k past events to support them as real events rather than as noise events in the correlation time τ, not just one. Wang et al [ 38 ] proposed an improved linear comb filter to remove event flickering, using both delay feedback and delay feedforward to obtain the delayed version response of the original signal, effectively passing the DC component of the signal while strongly attenuating all harmonics of the desired fundamental frequency. Since the filter is based on a linear filter design, there is a brief transient when the internal filter state converges to attenuate undesired signals.…”

Section: Introductionmentioning

confidence: 99%

Denoising Method Based on Salient Region Recognition for the Spatiotemporal Event Stream

Tang

Zhao

et al. 2023

Sensors

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Event cameras are the emerging bio-mimetic sensors with microsecond-level responsiveness in recent years, also known as dynamic vision sensors. Due to the inherent sensitivity of event camera hardware to light sources and interference from various external factors, various types of noises are inevitably present in the camera’s output results. This noise can degrade the camera’s perception of events and the performance of algorithms for processing event streams. Moreover, since the output of event cameras is in the form of address-event representation, efficient denoising methods for traditional frame images are no longer applicable in this case. Most existing denoising methods for event cameras target background activity noise and sometimes remove real events as noise. Furthermore, these methods are ineffective in handling noise generated by high-frequency flickering light sources and changes in diffused light reflection. To address these issues, we propose an event stream denoising method based on salient region recognition in this paper. This method can effectively remove conventional background activity noise as well as irregular noise caused by diffuse reflection and flickering light source changes without significantly losing real events. Additionally, we introduce an evaluation metric that can be used to assess the noise removal efficacy and the preservation of real events for various denoising methods.

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A Linear Comb Filter for Event Flicker Removal

Cited by 10 publications

References 28 publications

An Asynchronous Kalman Filter for Hybrid Event Cameras

An Asynchronous Kalman Filter for Hybrid Event Cameras

Frequency Cam: Imaging Periodic Signals in Real-Time

Denoising Method Based on Salient Region Recognition for the Spatiotemporal Event Stream

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