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New framework tackles asymmetric blur in smartphone stereo cameras

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced a new framework and dataset for addressing heterogeneous stereo deblurring, a problem arising from hardware variations in smartphone cameras that cause asymmetric blur. The proposed physically- and epipolar-constrained cross attention (PECA) module enhances feature fusion by adhering to physically valid disparity constraints and employing a confidence-weighted attention mechanism. This approach improves deblurring performance across various architectures like CNNs, Transformers, and NAFNet, demonstrating efficiency and reliability. AI

IMPACT This research could lead to improved image quality in mobile XR applications by addressing specific hardware-related image degradation.

RANK_REASON The cluster contains an academic paper detailing a new benchmark, dataset, and method for a computer vision task.

Read on arXiv cs.CV →

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

New framework tackles asymmetric blur in smartphone stereo cameras

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The cluster contains an academic paper detailing a new benchmark, dataset, and method for a computer vision task.
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94 days old
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Hoju Shin, Jiah Kim, Seung-Wook Kim, Seowon Ji ·

    A Benchmark for Heterogeneous Stereo Deblurring with Physically- and Epipolar-constrained Cross Attention

    arXiv:2606.25962v1 Announce Type: new Abstract: Modern stereo-capable smartphones enable immersive XR content capture. However, hardware heterogeneity across camera modules often causes severe asymmetric blur artifacts. Existing methods and benchmarks largely assume homogeneous s…

  2. arXiv cs.CV TIER_1 English(EN) · Seowon Ji ·

    A Benchmark for Heterogeneous Stereo Deblurring with Physically- and Epipolar-constrained Cross Attention

    Modern stereo-capable smartphones enable immersive XR content capture. However, hardware heterogeneity across camera modules often causes severe asymmetric blur artifacts. Existing methods and benchmarks largely assume homogeneous stereo setups and therefore do not explicitly add…

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