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English(EN) SR-Ground: Image Quality Grounding for Super-Resolved Content

新数据集SR-Ground增强了超分辨率图像质量评估

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了SR-Ground，这是一个新大规模数据集，专为超分辨率图像中的细粒度伪影分割而设计。该数据集具有多种伪影类别的像素级标注，旨在提高图像质量评估（IQA）模型的可解释性。通过在SR-Ground上训练具有基础能力的IQA模型，下游任务的性能得到显著提升，并且演示了一个微调流程，以减少超分辨率输出中可感知的伪影。 AI

影响 增强了超分辨率任务中图像质量评估模型的可解释性和有效性。

排序理由 该集群描述了一篇介绍数据集和方法论的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新数据集SR-Ground增强了超分辨率图像质量评估

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该集群描述了一篇介绍数据集和方法论的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Artem Borisov, Evgeney Bogatyrev, Khaled Abud, Dmitriy Vatolin ·

    SR-Ground: 超分辨率内容的图像质量接地

    arXiv:2605.21244v2 Announce Type: replace Abstract: 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) met…

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