A learnable nonlocal block that mimics classical neighbor matching and collaborative filtering on multiscale features produces competitive RAW denoising with far fewer parameters than current deep models and generalizes across sensors.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Q-Align trains LMMs on discrete text-defined levels for visual scoring, achieving SOTA on IQA, IAA, and VQA while unifying the tasks in OneAlign.
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Learned Nonlocal Feature Matching and Filtering for RAW Image Denoising
A learnable nonlocal block that mimics classical neighbor matching and collaborative filtering on multiscale features produces competitive RAW denoising with far fewer parameters than current deep models and generalizes across sensors.
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Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
Q-Align trains LMMs on discrete text-defined levels for visual scoring, achieving SOTA on IQA, IAA, and VQA while unifying the tasks in OneAlign.