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iPhoneBlur benchmark stratifies motion deblurring difficulty for consumer devices

Researchers have introduced iPhoneBlur, a new benchmark designed to evaluate motion blur restoration models on consumer devices. This benchmark consists of 7,400 image pairs synthesized from iPhone 17 Pro videos and is stratified into Easy, Medium, and Hard difficulty levels. The stratification reveals significant performance degradation across these levels, a gap often masked by aggregate metrics in existing evaluations. iPhoneBlur aims to enable more systematic assessment of model reliability for edge systems. AI

IMPACT Provides a more realistic evaluation framework for AI models deployed on consumer devices, highlighting performance limitations under varying conditions.

RANK_REASON The cluster contains a new academic paper introducing a benchmark dataset.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

iPhoneBlur benchmark stratifies motion deblurring difficulty for consumer devices

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The cluster contains a new academic paper introducing a benchmark dataset.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Abdullah Al Shafi, Kazi Saeed Alam ·

    iPhoneBlur: A Difficulty-Stratified Benchmark for Consumer Device Motion Deblurring

    arXiv:2605.05990v1 Announce Type: new 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 introduc…

  2. arXiv cs.CV TIER_1 English(EN) · Kazi Saeed Alam ·

    iPhoneBlur: A Difficulty-Stratified Benchmark for Consumer Device Motion Deblurring

    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…