English(EN) AQUA-Net: Adaptive Frequency Fusion and Illumination Aware Network for Underwater Image Enhancement

新型 AQUA-Net 模型通过频率和光照融合增强水下图像

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了 AQUA-Net，这是一种新颖的深度学习模型，旨在增强水下图像。该网络通过整合空间、频率和光照域处理来解决颜色失真和低对比度等挑战。AQUA-Net 采用带有辅助分支的残差编码器-解码器，利用傅里叶变换获取频率信息，并利用学习到的光照图进行自适应曝光校正。该模型在参数更少的情况下，与最先进的方法相比表现出竞争力，并且其有效性通过来自地中海的新高分辨率数据集得到了进一步验证。 AI

影响该模型为实时水下图像增强提供了更有效的解决方案，有望改进海洋研究、勘探和监控等应用。

排序理由该集群包含一篇详细介绍新模型和数据集的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Munsif Ali, Najmul Hassan, Lucia Ventura, Davide Di Bari, Simonepietro Canese, Jungpil Shin · 2026-10-19 04:00

AQUA-Net：用于水下图像增强的自适应频率融合和光照感知网络

arXiv:2512.05960v2 Announce Type: replace-cross Abstract: Underwater images often suffer from severe color distortion, low contrast, and a hazy appearance due to wavelength-dependent light absorption and scattering. Simultaneously, existing deep learning models exhibit high compu…

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AQUA-Net：用于水下图像增强的自适应频率融合和光照感知网络

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