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新的PRIN网络以感知和结构为重点增强低光图像

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为PRIN（感知-结构Retinex启发网络）的新网络，旨在增强低光图像。该方法将图像分解为反射率和亮度分量，并对每个分量应用专门的细化技术。反射率分支专注于保留纹理和边缘，而亮度分支确保亮度调整的一致性。在LOL-v1和LOL-v2等基准数据集上的实验证明了PRIN在提高结构相似性和感知质量方面的有效性。 AI

影响 这项研究引入了一种新颖的低光图像增强方法，有可能在各种应用中改善挑战性光照条件下的视觉质量。

排序理由 该项目是一篇研究论文，详细介绍了一种新的图像增强网络。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新的PRIN网络以感知和结构为重点增强低光图像

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该项目是一篇研究论文，详细介绍了一种新的图像增强网络。[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) · Qiyi Xie, Wei Chen Ng, Ziyao Li, Yuning Wu, Huasen Wu, Tianshi Miao, Jian Ding Tan ·

    PRIN: A Perceptual-Structural Retinex-Inspired Network for Low-Light Image Enhancement

    arXiv:2610.41338v1 Announce Type: cross Abstract: Low-light image enhancement (LLIE) focuses not only on visibility recovery but also on preserving structurally meaningful content and perceptual naturalness. Existing learning-based methods may achieve favorable pixel-wise fidelit…

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