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

• URL: http://arxiv.org/abs/2605.05990v1
• Date: Thu, 07 May 2026 10:41:16 GMT
• Title: iPhoneBlur: A Difficulty-Stratified Benchmark for Consumer Device Motion Deblurring
• Abstract summary: This work introduces iPhoneBlur, a difficulty-stratified benchmark of 7,400 image pairs synthesized from high-framerate iPhone 17 Pro videos.<n>Each sample includes comprehensive metadata enabling investigation of ISP-aware and difficulty-adaptive restoration strategies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• GBU-Palm: A Multimodal Video Dataset and Benchmark for Palm Presentation Attack Detection [73.00315875431374]
  GBU-Palm is a large-scale multimodal video dataset and benchmark containing 21,326 videos from 105 subjects and 210 palms.<n>We construct leakage-controlled protocols that separate palm identity and attack lineage and benchmark four representative video architectures.<n>Results reveal substantial architecture-dependent degradation under environmental shift and show that RGB-NIR fusion does not consistently outperform RGB-only input.
  arXiv  Detail & Related papers  (2026-08-14T15:31:48Z)
• RankVR: Low-Rank Structure Perception and Value Recalibration for Robust Composed Image Retrieval [24.383054290279173]
  RankVR is a framework designed to construct a robust Composed Image Retrieval model via global structure consistency and dynamic value perception.<n>It significantly outperforms existing state-of-the-art methods, validating its superior robustness in noisy environments.
  arXiv  Detail & Related papers  (2026-06-10T06:06:34Z)
• Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification [54.952203312050564]
  Adversarial attacks pose a severe threat to the reliability of deep learning models in remote sensing (RS) image classification.<n>We propose FogFool, a physically plausible adversarial framework that generates fog-based perturbations.<n>Experiments on two benchmark RS datasets demonstrate that FogFool achieves superior performance.
  arXiv  Detail & Related papers  (2026-04-16T05:47:57Z)
• Variational Rectification Inference for Learning with Noisy Labels [74.85528327499662]
  We propose variational rectification inference (VRI) to formulate the adaptive rectification for loss functions.<n>VRI is constructed as a hierarchical Bayes by treating the rectifying vector as a latent variable.<n>By introducing the variational term in VRI, the conditional posterior is estimated accurately and avoids collapsing to a Dirac delta function.
  arXiv  Detail & Related papers  (2026-03-18T01:25:08Z)
• Multi-Period Texture Contrast Enhancement for Low-Contrast Wafer Defect Detection and Segmentation [19.221722169368494]
  TexWDS is a texture-aware framework that harmonizes multi-scale feature retention with frequency-domain modeling.<n>By modeling texture disruptions in the frequency domain, MPTCE explicitly decouples non-periodic anomalies from structured backgrounds.<n>TexWDS achieves a new state-of-the-art, surpassing the baseline by 8.3% in mAP50-95 and 7.7% in recall, while reducing the false positive rate by approximately 8.6%.
  arXiv  Detail & Related papers  (2026-03-15T08:35:49Z)
• Efficient Semi-Automated Material Microstructure Analysis Using Deep Learning: A Case Study in Additive Manufacturing [0.0]
  We present a semi-automated active learning based segmentation that integrates a U-Net based convolutional neural network with an interactive user annotation and correction interface.<n>The proposed framework reduces labeling effort while maintaining scalability and robustness and is broadly applicable to image based analysis across diverse materials systems.
  arXiv  Detail & Related papers  (2026-03-14T08:28:00Z)
• Quality-Aware Robust Multi-View Clustering for Heterogeneous Observation Noise [12.720216418233795]
  We propose a novel framework termed Quality-Aware Robust Multi-View Clustering (QARMVC)<n>QARMVC employs an information bottleneck mechanism to extract intrinsic semantics for view reconstruction.<n>In experiments on five benchmark datasets, QARMVC consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-02-26T03:16:44Z)
• From Filters to VLMs: Benchmarking Defogging Methods through Object Detection and Segmentation Performance [2.0524609401792397]
  We present a structured empirical study that benchmarks a comprehensive set of pipelines.<n>We assess both image quality and downstream performance on object detection (mAP) and segmentation (PQ, RQ, SQ)<n>Our analysis reveals when defogging helps, when chaining yields synergy or degradation, and how VLM-based editors compare to dedicated approaches.
  arXiv  Detail & Related papers  (2025-10-04T19:05:04Z)
• Quantization Robustness to Input Degradations for Object Detection [2.0901018134712297]
  Post-training quantization (PTQ) is crucial for deploying efficient object detection models, like YOLO, on resource-constrained devices.<n>The impact of reduced precision on model robustness to real-world input degradations such as noise, blur, and compression artifacts is a significant concern.<n>This paper presents an empirical study evaluating the robustness of YOLO models across multiple precision formats.
  arXiv  Detail & Related papers  (2025-08-27T06:20:38Z)
• SOTA: Spike-Navigated Optimal TrAnsport Saliency Region Detection in Composite-bias Videos [50.51658520045165]
  Spike-d Optimal TrAnsport Saliency Region Detection (SOTA) is a framework that leverages the strengths of spike cameras while mitigating biases in both spatial and temporal dimensions.<n>Our method introduces Spike-based Micro-debias (SM) to capture subtle frame-to-frame variations.<n>SOTA refines predictions by reducing inconsistencies across diverse conditions.
  arXiv  Detail & Related papers  (2025-05-01T08:30:40Z)
• DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration [66.01846902242355]
  Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training. It is expensive and infeasible to include every type of degradation to cover real-world cases in the training data. We propose Robust Degradation Remover (DR2) to first transform the degraded image to a coarse but degradation-invariant prediction, then employ an enhancement module to restore the coarse prediction to a high-quality image.
  arXiv  Detail & Related papers  (2023-03-13T06:05:18Z)
• Designing a Practical Degradation Model for Deep Blind Image Super-Resolution [134.9023380383406]
  Single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images. This paper proposes to design a more complex but practical degradation model that consists of randomly shuffled blur, downsampling and noise degradations.
  arXiv  Detail & Related papers  (2021-03-25T17:40:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.