高效融合多帧高动态范围图像，挑战参数与算力双重限制
NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results
- 用九帧不同曝光的原始数据，设计轻量级融合模型
- 顶尖方案在3000万参数内实现43.22dB的重建质量
- 适合做移动端或实时HDR处理的研究者参考
本文回顾了NTIRE 2025高效多帧高动态范围（HDR）与图像修复挑战赛，旨在推动高效多帧HDR与修复技术的发展。挑战赛基于一个新型的RAW多帧融合数据集，每场景包含九张噪声大且未对齐的RAW图像，覆盖多种曝光水平。参赛者需在严格效率约束下完成融合：模型参数少于3000万，计算量低于4.0万亿次浮点运算（FLOPs）。共有217人注册，最终六支队伍提交有效方案。最优方法达到43.22 dB的峰值信噪比（PSNR），展现了新方法在此领域的潜力。本文全面综述了挑战内容，对比了各方案，并为高效多帧HDR与修复研究者提供了重要参考。
原文摘要 · Abstract (English)
This paper reviews the NTIRE 2025 Efficient Burst HDR and Restoration Challenge, which aims to advance efficient multi-frame high dynamic range (HDR) and restoration techniques. The challenge is based on a novel RAW multi-frame fusion dataset, comprising nine noisy and misaligned RAW frames with various exposure levels per scene. Participants were tasked with developing solutions capable of effectively fusing these frames while adhering to strict efficiency constraints: fewer than 30 million model parameters and a computational budget under 4.0 trillion FLOPs. A total of 217 participants registered, with six teams finally submitting valid solutions. The top-performing approach achieved a PSNR of 43.22 dB, showcasing the potential of novel methods in this domain. This paper provides a comprehensive overview of the challenge, compares the proposed solutions, and serves as a valuable reference for researchers and practitioners in efficient burst HDR and restoration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。