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BurstMamba architecture optimizes image super-resolution with efficient frame processing

Researchers have introduced BurstMamba, a novel architecture for burst image super-resolution (BISR). This method optimizes computation by focusing processing power on reconstructing a high-resolution keyframe while using a lightweight stream to extract sub-pixel information from other frames. Key innovations include a Gather-Aggregate-Scatter (GAS) mechanism for efficient cross-frame message passing and wavelet-conditioned state updates to prioritize high-frequency regions. BurstMamba demonstrates state-of-the-art performance on multiple benchmark datasets, with ablations confirming the effectiveness of its compute separation and feature extraction techniques. AI

IMPACT Introduces a novel architecture for image super-resolution that optimizes computational efficiency and performance.

RANK_REASON The item is a research paper detailing a new model architecture for image super-resolution. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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BurstMamba architecture optimizes image super-resolution with efficient frame processing

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The item is a research paper detailing a new model architecture for image super-resolution. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ozan Unal, Steven Marty, Dengxin Dai ·

    Keyframe-Centric State-Space Modeling for Burst Image Super-Resolution

    arXiv:2503.19634v2 Announce Type: replace Abstract: Burst image super-resolution (BISR) reconstructs a high-resolution keyframe by aggregating complementary sub-pixel evidence from a short burst of low-resolution frames. Existing methods often process all burst frames with heavy …