为手机端运动模糊去模糊设计了分难度的基准测试。
iPhoneBlur: A Difficulty-Stratified Benchmark for Consumer Device Motion Deblurring
- 按模糊程度分易、中、难三类，基于PSNR自适应划分。
- 模型在难样本上性能比易样本下降7-9 dB， aggregate指标掩盖此差异。
- 适合研究边缘设备上模型可靠性与失败模式的开发者。
消费级手机端运动模糊恢复通常依赖聚合指标，掩盖了不同模糊难度下的性能差异，无法反映真实部署场景中的模型表现。本文提出iPhoneBlur，一个由7400对图像组成的分难度基准，数据源自真实世界场景下高帧率iPhone 17 Pro视频合成。通过PSNR引导的自适应时间窗口，将样本划分为易、中、难三类，各层级光学流幅值单调提升2.2倍，验证了分层有效性。每对图像包含丰富元数据，支持ISP感知与难度自适应修复策略研究。频谱分析表明合成模糊具有与真实运动退化一致的高频抑制特征。对六种架构的评估显示，从易到难子集性能持续下降7-9 dB，该显著差距被传统聚合报告完全掩盖。基准还揭示了专业与消费级相机间的领域差异，针对性微调可显著弥补。通过结合难度分层与部署关键元数据，iPhoneBlur支持资源受限边缘系统中模型可靠性和失效模式的系统性评估。
原文摘要 · Abstract (English)
Motion blur restoration on consumer mobile devices is typically evaluated using aggregate metrics that obscure performance variation across blur difficulty, masking model behavior under real deployment conditions. This work introduces iPhoneBlur, a difficulty-stratified benchmark of 7,400 image pairs synthesized from high-framerate iPhone 17 Pro videos captured in diverse real-world scenarios. Samples are partitioned into Easy, Medium, and Hard categories through PSNR-guided adaptive temporal windowing, with stratification validated by monotonic 2.2x increase in optical flow magnitude across tiers. Each sample includes comprehensive metadata enabling investigation of ISP-aware and difficulty-adaptive restoration strategies. Spectral analysis confirms synthesized blur exhibits high-frequency suppression patterns consistent with authentic motion degradation. Evaluation of six architectures reveals consistent 7-9 dB performance degradation from Easy to Hard subsets, a substantial gap entirely hidden by aggregate reporting. The benchmark further exposes a domain gap between professional and consumer cameras which targeted fine-tuning substantially recovers. By coupling difficulty stratification with deployment-critical metadata, iPhoneBlur enables systematic assessment of model reliability and failure modes for resource-constrained edge systems.
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