标题： 通过深度学习增强水下图像：基于VGG19和ResNet50的方法比较研究 显示英文标题

标题： Enhancing Underwater Images via Deep Learning: A Comparative Study of VGG19 and ResNet50-Based Approaches

摘要： 本文针对复杂水下场景中的图像增强这一具有挑战性的问题，提出了一种基于深度学习的解决方案。所提出的方法巧妙地整合了两个深度卷积神经网络模型，VGG19 和 ResNet50，利用它们强大的特征提取能力对水下图像进行多尺度和多层级的深度特征分析。通过构建一个统一的模型，有效整合了两种模型的互补优势，实现了更全面和准确的图像增强效果。为了客观评估增强效果，本文引入了图像质量评估指标如 PSNR、UCIQE 和 UIQM，对增强前后的图像进行定量比较，并深入分析了不同模型在不同场景下的性能。此外，为了提高水下视觉增强系统的实用性和稳定性，本文还从模型优化、多模型融合和硬件选择等方面提出了实用建议，旨在为复杂水下环境中的视觉增强任务提供强有力的技术支持。 显示英文摘要

摘要： This paper addresses the challenging problem of image enhancement in complex underwater scenes by proposing a solution based on deep learning. The proposed method skillfully integrates two deep convolutional neural network models, VGG19 and ResNet50, leveraging their powerful feature extraction capabilities to perform multi-scale and multi-level deep feature analysis of underwater images. By constructing a unified model, the complementary advantages of the two models are effectively integrated, achieving a more comprehensive and accurate image enhancement effect.To objectively evaluate the enhancement effect, this paper introduces image quality assessment metrics such as PSNR, UCIQE, and UIQM to quantitatively compare images before and after enhancement and deeply analyzes the performance of different models in different scenarios.Furthermore, to improve the practicality and stability of the underwater visual enhancement system, this paper also provides practical suggestions from aspects such as model optimization, multi-model fusion, and hardware selection, aiming to provide strong technical support for visual enhancement tasks in complex underwater environments.