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English(EN) Cue-guided dual-path reconstruction with adaptive multi-scale response refinement for low-light image enhancement

新网络通过自适应细化增强低光图像

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了CueDR-Net，一种旨在增强低光条件下拍摄图像的新型双路径网络。该网络通过结合亮度调整和局部内容重建来解决颜色偏差和细节模糊等问题。它利用双向提示交互和自适应多尺度响应细化卷积（AMRRConv）来提高特征提取和预测精度。在基准数据集上的评估显示，在图像增强和低光检测方面具有竞争力。 AI

影响 这项研究提出了一种低光图像增强的新方法，有可能在各种应用中改善挑战性条件下的视觉数据质量。

排序理由 该集群包含一篇详细介绍图像增强新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

新网络通过自适应细化增强低光图像

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该集群包含一篇详细介绍图像增强新模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yicui Shi, Yuhan Chen, Xiangfei Huang, Zhenguo Wang, Wenxuan Yu, Ying Fang, Jie Li, Guofa Li ·

    用于低光图像增强的引导式双路径重建与自适应多尺度响应细化

    arXiv:2603.16482v2 Announce Type: replace-cross Abstract: Low-light image enhancement (LLIE) supports optical sensing and scene observation when limited photon capture and sensor noise degrade image quality. However, existing methods can introduce colour deviations or blur local …

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