用VGG19和ResNet50融合提升水下图像质量
Enhancing Underwater Images via Deep Learning: A Comparative Study of VGG19 and ResNet50-Based Approaches
- 融合VGG19与ResNet50进行多尺度特征提取
- 在复杂水下场景中实现显著的清晰度提升
- 适合水下视觉系统开发与优化参考
针对复杂水下场景的图像增强难题，本文提出基于深度学习的解决方案，结合VGG19与ResNet50两种深度卷积神经网络，利用其强大的特征提取能力，对水下图像进行多尺度、多层次的深度特征分析。通过构建统一模型，有效整合两者互补优势，实现更全面、精准的图像增强效果。为客观评估增强效果，引入PSNR、UCIQE和UIQM等图像质量评价指标，定量比较增强前后图像表现，并深入分析不同模型在各类场景下的性能。此外，为提升水下视觉增强系统的实用性与稳定性，本文还从模型优化、多模型融合及硬件选型等方面提出具体建议，旨在为复杂水下环境中的视觉增强任务提供有力技术支撑。
原文摘要 · Abstract (English)
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.
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