iPhoneBlur: A Difficulty-Stratified Benchmark for Consumer Device Motion Deblurring
📰 ArXiv cs.AI
Learn about iPhoneBlur, a benchmark for evaluating motion deblurring models on consumer devices, and how it helps assess performance across varying blur difficulties
Action Steps
- Synthesize image pairs from high-framerate videos to create a benchmark dataset
- Partition the dataset into Easy, Medium, and Hard categories based on blur difficulty
- Evaluate motion deblurring models using the iPhoneBlur benchmark to assess performance variation across blur difficulties
- Compare model performance on the iPhoneBlur benchmark to identify areas for improvement
- Use the iPhoneBlur benchmark to fine-tune and optimize motion deblurring models for real-world deployment
Who Needs to Know This
Computer vision engineers and researchers working on motion deblurring models can benefit from this benchmark to evaluate and improve their models' performance in real-world scenarios
Key Insight
💡 iPhoneBlur provides a comprehensive benchmark for evaluating motion deblurring models' performance across varying blur difficulties, enabling more accurate assessments and improvements
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📸 Introducing iPhoneBlur, a difficulty-stratified benchmark for evaluating motion deblurring models on consumer devices 📊
Key Takeaways
Learn about iPhoneBlur, a benchmark for evaluating motion deblurring models on consumer devices, and how it helps assess performance across varying blur difficulties
Full Article
Title: iPhoneBlur: A Difficulty-Stratified Benchmark for Consumer Device Motion Deblurring
Abstract:
arXiv:2605.05990v1 Announce Type: cross Abstract: 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 c
Abstract:
arXiv:2605.05990v1 Announce Type: cross Abstract: 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 c
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