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English(EN) FLARE-BO: Fused Luminance and Adaptive Retinex Enhancement via Bayesian Optimisation for Low-Light Robotic Vision

新方法利用视网膜和贝叶斯优化增强低光图像

研究人员开发了FLARE-BO,一个用于改进低光机器人视觉的增强框架。该新方法通过优化八个参数(包括伽马校正、光照归一化和白平衡)来扩展先前无训练的方法。FLARE-BO系统利用高斯过程的贝叶斯优化,为单个图像自适应地调整这些参数,在低光配对数据集上表现优于现有方法。 AI

影响 增强机器人的低光视觉能力,可能提高在挑战性环境中的导航和检测能力。

排序理由 这是一篇详细介绍图像增强新方法的学术论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新方法利用视网膜和贝叶斯优化增强低光图像

报道来源 [3]

  1. arXiv cs.CV TIER_1 English(EN) · Alexandru Brateanu, Tingting Mu, Codruta Ancuti, Cosmin Ancuti ·

    Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex

    arXiv:2604.10359v2 Announce Type: replace Abstract: Low-light image enhancement (LLIE) aims to restore natural visibility, color fidelity, and structural detail under severe illumination degradation. State-of-the-art (SOTA) LLIE techniques often rely on large models and multi-sta…

  2. arXiv cs.CV TIER_1 English(EN) · Nathan Shankar, Pawel Ladosz, Hujun Yin ·

    FLARE-BO: Fused Luminance and Adaptive Retinex Enhancement via Bayesian Optimisation for Low-Light Robotic Vision

    arXiv:2604.22093v1 Announce Type: new Abstract: Reliable visual perception under low illumination remains a core challenge for autonomous robotic systems, where degraded image quality directly compromises navigation, inspection, and various operations. A recent training free appr…

  3. arXiv cs.CV TIER_1 English(EN) · Hujun Yin ·

    FLARE-BO: Fused Luminance and Adaptive Retinex Enhancement via Bayesian Optimisation for Low-Light Robotic Vision

    Reliable visual perception under low illumination remains a core challenge for autonomous robotic systems, where degraded image quality directly compromises navigation, inspection, and various operations. A recent training free approach showed that Bayesian optimisation with Gaus…