Event-based High Dynamic Range Imaging for Dynamic Scenes with Motion Blur

Conceitos Básicos

The core message of this article is to propose a self-supervised learning framework, called Self-EHDRI, to efficiently reconstruct sharp high dynamic range (HDR) images from blurry low dynamic range (LDR) images and concurrent event streams, without requiring ground-truth sharp HDR images.

Resumo

The article presents a novel self-supervised learning framework, called Self-EHDRI, to address the challenge of high dynamic range imaging (HDRI) for real-world dynamic scenes. The key highlights are:

The proposed framework consists of two main components:

The Event-based Blurry LDR to Sharp HDR (E-BL2SH) network, which performs joint HDRI and motion deblurring in an end-to-end manner.
The Dynamic Range Decomposition (DRD) and Dynamic Range Composition (DRC) networks, which facilitate self-supervised learning by enabling flexible conversion between HDR and LDR domains.

The self-supervised learning strategy is carried out by learning cross-domain conversions from blurry LDR images to sharp LDR images, which enables sharp HDR images to be accessible in the intermediate process even though ground-truth sharp HDR images are missing.

The framework leverages both the high dynamic range and high temporal resolution of events to compensate for the hybrid degradation of LDR and motion blur, outperforming state-of-the-art cascading approaches.

A large-scale benchmark dataset, BL2SHD, is introduced, comprising synthetic and real-world data with blurry LDR images, event streams, and sharp LDR observations, to evaluate the effectiveness of the proposed method.

Comprehensive experiments demonstrate that the proposed Self-EHDRI significantly outperforms state-of-the-art methods on both quantitative and qualitative evaluations.

Estatísticas

"Due to the limited dynamic range of conventional cameras, modern photography in real-world scenarios often suffers from over- or under-exposures, leading to Low Dynamic Range (LDR) images with intensity saturation."
"Event cameras record brightness changes in microsecond resolution, thus making it feasible to reconstruct latent sharp frames even under nonlinear motions."

Citações

"Optimization for HDRI in highly dynamic scenes requires sharp HDR images, which are difficult to obtain."
"To achieve HDRI in general dynamic scenes, we propose a Self-supervised Event-based HDRI framework, i.e., Self-EHDRI, decoupling the hybrid degradation by learning an Event-based Blurry LDR to Sharp HDR network, i.e., E-BL2SH for joint HDRI and motion deblurring in the LDR domain."

Principais Insights Extraídos De

HDR Imaging for Dynamic Scenes with Events

by Li Xiaopeng,... às arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03210.pdf

Perguntas Mais Profundas

How can the proposed self-supervised learning framework be extended to other computer vision tasks that require ground-truth data that is difficult to obtain, such as depth estimation or semantic segmentation in dynamic scenes

The proposed self-supervised learning framework can be extended to other computer vision tasks that require ground-truth data that is difficult to obtain, such as depth estimation or semantic segmentation in dynamic scenes. One way to adapt the framework for depth estimation is by formulating a self-supervised learning strategy similar to Self-EHDRI. Instead of relying on ground-truth depth data, the framework can learn cross-domain conversions from blurry depth maps to sharp depth maps. By leveraging the high temporal resolution of events and incorporating self-supervised losses, the network can be trained to reconstruct accurate depth maps in dynamic scenes without the need for ground-truth data.
For semantic segmentation in dynamic scenes, the self-supervised framework can be modified to learn the mapping from blurry semantic segmentation masks to sharp masks. The network can utilize the information from event streams and blurry images to generate high-quality semantic segmentation results. By incorporating self-supervised consistencies and optimizing the network in an end-to-end manner, the framework can adapt to challenging real-world scenarios where obtaining ground-truth data is impractical.

What are the potential limitations of the event-based approach, and how can they be addressed to further improve the performance in challenging real-world scenarios

One potential limitation of the event-based approach is the noise and sparsity inherent in event data, which can affect the quality of the reconstructed images. To address this limitation and further improve performance in challenging real-world scenarios, several strategies can be implemented:

Noise Reduction Techniques: Implement noise reduction algorithms to enhance the quality of event data before feeding it into the network. Denoising methods such as Gaussian filtering or deep learning-based denoising networks can help improve the accuracy of event-based imaging.

Multi-Sensor Fusion: Integrate event-based cameras with other sensing modalities such as lidar or radar to complement the information captured by each sensor. By fusing data from multiple sensors, the system can achieve more comprehensive scene understanding and improve performance in complex scenarios.

Adaptive Event Processing: Develop adaptive event processing algorithms that can dynamically adjust the sensitivity and threshold levels based on the scene dynamics. This can help optimize the event data for different scenarios and improve the robustness of the system.

Advanced Event Processing Networks: Explore the use of advanced neural network architectures specifically designed for event-based data processing. Techniques such as spatiotemporal convolutional networks or recurrent neural networks can better capture the temporal dynamics of events and improve the overall performance of event-based imaging systems.

Given the advancements in event-based imaging, how might this technology be integrated with other emerging sensing modalities, such as lidar or radar, to enable more comprehensive and robust scene understanding

The advancements in event-based imaging technology present opportunities for integration with other emerging sensing modalities like lidar or radar to enable more comprehensive and robust scene understanding. Here are some ways in which event-based imaging can be integrated with lidar or radar:

Sensor Fusion: Combine data from event-based cameras, lidar, and radar sensors to create a more holistic representation of the environment. Sensor fusion techniques can leverage the strengths of each modality to overcome individual limitations and provide a more accurate perception of the scene.

Complementary Information: Event-based cameras excel at capturing fast motion and high dynamic range scenes, while lidar and radar sensors provide precise distance and velocity measurements. By integrating these modalities, the system can benefit from the complementary information to enhance object detection, tracking, and scene understanding.

Multi-Modal Calibration: Develop calibration methods to ensure accurate alignment and synchronization between event-based cameras, lidar, and radar sensors. Proper calibration is essential for effective sensor fusion and reliable data interpretation in complex scenarios.

Real-Time Processing: Implement real-time processing algorithms that can efficiently handle data streams from multiple sensors. By integrating event-based imaging with lidar and radar in a real-time processing pipeline, the system can respond quickly to dynamic changes in the environment and support time-critical applications.