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New dataset SR-Ground targets super-resolution image artifacts

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SR-Ground, a new dataset designed to improve image quality assessment for super-resolved images. This dataset features pixel-level annotations for various artifact types introduced by modern super-resolution models. By training models on SR-Ground, researchers have shown improved performance in identifying and even reducing these artifacts, demonstrating practical applications for the dataset. AI

IMPACT This dataset could lead to more reliable and interpretable image quality assessment for AI-generated images, improving user trust and downstream applications.

RANK_REASON The cluster contains an academic paper detailing a new dataset and methodology for image artifact analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset SR-Ground targets super-resolution image artifacts

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The cluster contains an academic paper detailing a new dataset and methodology for image artifact analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Dmitriy Vatolin ·

    SR-Ground: Image Quality Grounding for Super-Resolved Content

    Super-Resolution (SR) has advanced rapidly in recent years, with diffusion-based models achieving unprecedented fidelity at the cost of introducing new types of visual artifacts. While existing Image Quality Assessment (IQA) methods provide holistic quality scores, they lack inte…

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