Reconstructing High-Quality HDR Video from Alternating Exposure Sequences: A Large-Scale Real-World Benchmark Dataset and a Two-Stage Alignment Network

Conceptos Básicos

This work presents a large-scale real-world dataset, Real-HDRV, to facilitate the development of HDR video reconstruction techniques, and proposes a two-stage alignment network to effectively handle complex motion and reconstruct high-quality HDR video.

Resumen

This paper addresses the problem of HDR video reconstruction from sequences with alternating exposures. The key contributions are:

Real-HDRV Dataset:

Constructed a large-scale real-world dataset for HDR video reconstruction, featuring various scenes, diverse motion patterns, and high-quality labels.
The dataset contains 500 LDRs-HDRs video pairs, comprising about 28,000 LDR frames and 4,000 HDR labels, covering diverse indoor/outdoor, daytime/nighttime scenes.
Compared to existing datasets, Real-HDRV provides more diverse scenes and motion patterns, enabling better generalization of trained models.

Two-Stage Alignment Network:

Proposed an end-to-end network for HDR video reconstruction, with a novel two-stage strategy to perform alignment sequentially.
The global alignment module (GAM) effectively handles global motion by adaptively estimating global offsets.
The local alignment module (LAM) implicitly performs local alignment in a coarse-to-fine manner at the feature level using adaptive separable convolution.
The two-stage alignment network can effectively handle complex motion and reconstruct high-quality HDR video.

Extensive experiments demonstrate that models trained on the Real-HDRV dataset outperform those trained on synthetic datasets when evaluated on real-world scenes. The proposed two-stage alignment network also outperforms previous state-of-the-art methods.

Estadísticas

The proposed Real-HDRV dataset contains 500 LDRs-HDRs video pairs, comprising about 28,000 LDR frames and 4,000 HDR labels.
The dataset covers diverse indoor/outdoor, daytime/nighttime scenes with various motion patterns, including global motion, local motion, and full motion.

Citas

"Models trained on our dataset can achieve better performance on real scenes than those trained on synthetic datasets."
"Our two-stage alignment network can effectively handle complex motion and reconstruct high-quality HDR video."

Ideas clave extraídas de

Towards Real-World HDR Video Reconstruction: A Large-Scale Benchmark Dataset and A Two-Stage Alignment Network

by Yong Shu,Liq... a las arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00244.pdf

Consultas más profundas

How can the proposed two-stage alignment network be extended to handle more than two alternating exposures?

The proposed two-stage alignment network can be extended to handle more than two alternating exposures by modifying the input processing and alignment stages. Instead of considering only three frames (one reference frame and two neighboring frames), the network can be adjusted to take in a larger number of frames with varying exposures. The global alignment module can be designed to handle the alignment of multiple frames with different global motions, while the local alignment module can be adapted to work with a larger set of frames for more complex local motions. By adjusting the architecture and training process to accommodate a greater number of alternating exposures, the network can effectively align and reconstruct HDR videos from sequences with multiple exposures.

What are the potential applications of the Real-HDRV dataset beyond HDR video reconstruction?

The Real-HDRV dataset has several potential applications beyond HDR video reconstruction:

HDR Image Processing: The dataset can be used for tasks like HDR image enhancement, HDR image fusion, and HDR image deghosting.
Computer Vision Research: Researchers can use the dataset for tasks like image registration, image alignment, and motion estimation in dynamic scenes.
Machine Learning: The dataset can serve as a benchmark for training and evaluating deep learning models for various computer vision tasks beyond HDR, such as image classification, object detection, and semantic segmentation.
Video Analytics: The dataset can be utilized for video analysis applications like action recognition, object tracking, and scene understanding in high dynamic range videos.
Virtual Reality and Augmented Reality: The dataset can be valuable for creating realistic and immersive VR/AR experiences by providing high-quality HDR content for rendering and visualization.

How can the proposed methods be adapted to handle real-time HDR video reconstruction for applications like live streaming or video conferencing?

To adapt the proposed methods for real-time HDR video reconstruction in applications like live streaming or video conferencing, several optimizations and adjustments can be made:

Efficient Network Architecture: Design a lightweight network architecture that can process frames in real-time without compromising on reconstruction quality.
Parallel Processing: Implement parallel processing techniques to distribute the computational load across multiple processing units or GPUs for faster inference.
Hardware Acceleration: Utilize hardware accelerators like GPUs, TPUs, or FPGAs to speed up the computation of alignment and reconstruction tasks.
Frame Skipping: Implement a frame skipping mechanism to prioritize processing key frames for alignment and reconstruction, reducing latency in the real-time pipeline.
Streaming Optimization: Integrate the reconstruction process with the streaming pipeline to ensure seamless delivery of HDR content in real-time.
Dynamic Exposure Adjustment: Develop algorithms to dynamically adjust exposure settings based on the input frames to optimize HDR reconstruction in varying lighting conditions.
By incorporating these optimizations and considerations, the proposed methods can be tailored for real-time HDR video reconstruction applications, ensuring efficient processing and delivery of high-quality HDR content in live streaming or video conferencing scenarios.