3,014 research outputs found
CED: Color Event Camera Dataset
Event cameras are novel, bio-inspired visual sensors, whose pixels output
asynchronous and independent timestamped spikes at local intensity changes,
called 'events'. Event cameras offer advantages over conventional frame-based
cameras in terms of latency, high dynamic range (HDR) and temporal resolution.
Until recently, event cameras have been limited to outputting events in the
intensity channel, however, recent advances have resulted in the development of
color event cameras, such as the Color-DAVIS346. In this work, we present and
release the first Color Event Camera Dataset (CED), containing 50 minutes of
footage with both color frames and events. CED features a wide variety of
indoor and outdoor scenes, which we hope will help drive forward event-based
vision research. We also present an extension of the event camera simulator
ESIM that enables simulation of color events. Finally, we present an evaluation
of three state-of-the-art image reconstruction methods that can be used to
convert the Color-DAVIS346 into a continuous-time, HDR, color video camera to
visualise the event stream, and for use in downstream vision applications.Comment: Conference on Computer Vision and Pattern Recognition Workshop
Event-Based Fusion for Motion Deblurring with Cross-modal Attention
Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times. As a kind of bio-inspired camera, the event camera records the intensity changes in an asynchronous way with high temporal resolution, providing valid image degradation information within the exposure time. In this paper, we rethink the event-based image deblurring problem and unfold it into an end-to-end two-stage image restoration network. To effectively fuse event and image features, we design an event-image cross-modal attention module applied at multiple levels of our network, which allows to focus on relevant features from the event branch and filter out noise. We also introduce a novel symmetric cumulative event representation specifically for image deblurring as well as an event mask gated connection between the two stages of our network which helps avoid information loss. At the dataset level, to foster event-based motion deblurring and to facilitate evaluation on challenging real-world images, we introduce the Real Event Blur (REBlur) dataset, captured with an event camera in an illumination controlled optical laboratory. Our Event Fusion Network (EFNet) sets the new state of the art in motion deblurring, surpassing both the prior best-performing image-based method and all event-based methods with public implementations on the GoPro dataset (by up to 2.47dB) and on our REBlur dataset, even in extreme blurry conditions. The code and our REBlur dataset will be made publicly available
Event-Based Fusion for Motion Deblurring with Cross-modal Attention
Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times. As a kind of bio-inspired camera, the event camera records the intensity changes in an asynchronous way with high temporal resolution, providing valid image degradation information within the exposure time. In this paper, we rethink the event-based image deblurring problem and unfold it into an end-to-end two-stage image restoration network. To effectively fuse event and image features, we design an event-image cross-modal attention module applied at multiple levels of our network, which allows to focus on relevant features from the event branch and filter out noise. We also introduce a novel symmetric cumulative event representation specifically for image deblurring as well as an event mask gated connection between the two stages of our network which helps avoid information loss. At the dataset level, to foster event-based motion deblurring and to facilitate evaluation on challenging real-world images, we introduce the Real Event Blur (REBlur) dataset, captured with an event camera in an illumination controlled optical laboratory. Our Event Fusion Network (EFNet) sets the new state of the art in motion deblurring, surpassing both the prior best-performing image-based method and all event-based methods with public implementations on the GoPro dataset (by up to 2.47dB) and on our REBlur dataset, even in extreme blurry conditions. The code and our REBlur dataset will be made publicly available
Neuromorphic Imaging with Joint Image Deblurring and Event Denoising
Neuromorphic imaging reacts to per-pixel brightness changes of a dynamic
scene with high temporal precision and responds with asynchronous streaming
events as a result. It also often supports a simultaneous output of an
intensity image. Nevertheless, the raw events typically involve a great amount
of noise due to the high sensitivity of the sensor, while capturing fast-moving
objects at low frame rates results in blurry images. These deficiencies
significantly degrade human observation and machine processing. Fortunately,
the two information sources are inherently complementary -- events with
microsecond temporal resolution, which are triggered by the edges of objects
that are recorded in latent sharp images, can supply rich motion details
missing from the blurry images. In this work, we bring the two types of data
together and propose a simple yet effective unifying algorithm to jointly
reconstruct blur-free images and noise-robust events, where an
event-regularized prior offers auxiliary motion features for blind deblurring,
and image gradients serve as a reference to regulate neuromorphic noise
removal. Extensive evaluations on real and synthetic samples present our
superiority over other competing methods in restoration quality and greater
robustness to some challenging realistic scenarios. Our solution gives impetus
to the improvement of both sensing data and paves the way for highly accurate
neuromorphic reasoning and analysis.Comment: Submitted to TI
EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning
Event cameras sense intensity changes and have many advantages over
conventional cameras. To take advantage of event cameras, some methods have
been proposed to reconstruct intensity images from event streams. However, the
outputs are still in low resolution (LR), noisy, and unrealistic. The
low-quality outputs stem broader applications of event cameras, where high
spatial resolution (HR) is needed as well as high temporal resolution, dynamic
range, and no motion blur. We consider the problem of reconstructing and
super-resolving intensity images from LR events, when no ground truth (GT) HR
images and down-sampling kernels are available. To tackle the challenges, we
propose a novel end-to-end pipeline that reconstructs LR images from event
streams, enhances the image qualities and upsamples the enhanced images, called
EventSR. For the absence of real GT images, our method is primarily
unsupervised, deploying adversarial learning. To train EventSR, we create an
open dataset including both real-world and simulated scenes. The use of both
datasets boosts up the network performance, and the network architectures and
various loss functions in each phase help improve the image qualities. The
whole pipeline is trained in three phases. While each phase is mainly for one
of the three tasks, the networks in earlier phases are fine-tuned by respective
loss functions in an end-to-end manner. Experimental results show that EventSR
reconstructs high-quality SR images from events for both simulated and
real-world data.Comment: Accepted by CVPR 202
Bringing Blurry Images Alive: High-Quality Image Restoration and Video Reconstruction
Consumer-level cameras are affordable for customers. While handy and easy to use, images and videos are likely to suffer from motion blur effect, especially under low-lighting conditions. Moreover, it is rather difficult to take high frame-rate videos due to the hardware limitations of conventional RGB-sensors. Therefore, our thesis mainly focuses on restoring high-quality (sharp, and high frame-rate) images and videos, from the low-quality (blur, and low frame-rate) ones for better practical applications. In this thesis, we mainly address the problem of how to restore a sharp image from a blurred stereo video sequence, a blurred RGB-D image, or a single blurred image. Then, by utilizing the faithful information about the motion provided by blurry effects in the image, we reconstruct high frame-rate and sharp videos based on an event camera, that brings blurry frame alive.
Stereo camera systems can provide motion information incorporated to help to remove complex spatially-varying motion blur in dynamic scenes. Given consecutive blurred stereo video frames, we recover the latent images, estimate the 3D scene flow, and segment the multiple moving objects simultaneously. We represent the dynamic scenes with the piece-wise planar model, which exploits the local structure of the scene and expresses various dynamic scenes. These three tasks are naturally connected under our model and expressed as the parameter estimation of 3D scene structure and camera motion (structure and motion for the dynamic scenes).
To tackle the challenging, minimal image deblurring case, namely, single-image deblurring, we first focus on blur caused by camera shake during the exposure time. We propose to jointly estimate the 6 DoF camera motion and remove the non-uniform blur by exploiting their underlying geometric relationships, with a single blurred RGB-D image as input. We formulate our joint deblurring and 6 DoF camera motion estimation as an energy minimization problem solved in an alternative manner.
In general cases, we solve the single-image deblurring task by studying the problem in the frequency domain. We show that the auto-correlation of the absolute phase-only image (phase-only image means the image is reconstructed only from the phase information of the blurry image) can provide faithful information about the motion (e.g., the motion direction and magnitude) that caused the blur, leading to a new and efficient blur kernel estimation approach.
Event cameras are gaining attention for they measure intensity changes (called `events') with microsecond accuracy. The event camera allows the simultaneous output of the intensity frames. However, the images are captured at a relatively low frame-rate and often suffer from motion blur. A blurred image can be regarded as the integral of a sequence of latent images, while the events indicate the changes between the latent images. Therefore, we model the blur-generation process by associating event data to a latent image. We propose a simple and effective approach, the EDI model, to reconstruct a high frame-rate, sharp video (>1000 fps) from a single blurry frame and its event data. The video generation is based on solving a simple non-convex optimization problem in a single scalar variable.
Then, we improved the EDI model by using multiple images and their events to handle flickering effects and noise in the generated video. Also, we provide a more efficient solver to minimize the proposed energy model.
Last, the blurred image and events also contribute to optical flow estimation. We propose a single image and events based optical flow estimation approach to unlock their potential applications.
In summary, this thesis addresses how to recover sharp images from blurred ones and reconstruct a high temporal resolution video from a single image and event. Our extensive experimental results demonstrate our proposed methods outperform the state-of-the-art
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