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English(EN) MeanSR: Restoration Trajectory Learning for One-Step Perceptual Super-Resolution

MeanSR方法通过学习的流场推进感知超分辨率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的一步感知超分辨率方法MeanSR，该方法学习一个低分辨率图像条件下的平均流场，直接从退化输入生成高分辨率图像。该方法旨在比以前的方法更有效地捕捉过渡动力学。据报道，MeanSR在CLIPIQA、MUSIQ和MANIQA等基准测试中优于CTMSR等现有技术，同时还降低了计算成本并缩短了推理时间。该方法旨在以更少的伪影产生更清晰的结构和更逼真的纹理。 AI

影响 引入了一种更有效、更高效的图像超分辨率方法，可能改进媒体和成像领域的应用。

排序理由 该集群包含一篇详细介绍一种新图像超分辨率方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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MeanSR方法通过学习的流场推进感知超分辨率

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该集群包含一篇详细介绍一种新图像超分辨率方法的论文。[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) · Axi Niu, Jiawei Kou, Kang Zhang, Qingsen Yan, Jinqiu Sun, Yanning Zhang ·

    MeanSR：一步感知超分辨率的恢复轨迹学习

    arXiv:2608.09405v1 Announce Type: new Abstract: Diffusion-based super-resolution (SR) achieves strong perceptual quality but requires costly iterative denoising. Existing one-step distillation methods reduce inference time but depend on expensive pretrained teachers, whereas CTMS…

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