Abstract
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 this http URL 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 this http URL, 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.
Abstract (translated)
本文提出了一种基于深度学习的解决方案，旨在解决复杂水下场景中的图像增强难题。所提方法巧妙地结合了VGG19和ResNet50两个深度卷积神经网络模型，利用它们强大的特征提取能力对水下图像进行多尺度、多层次的深入特征分析。通过构建统一模型，有效整合了两者的互补优势，从而实现更加全面且准确的图像增强效果。 为了客观评估增强效果，本文引入了PSNR（峰值信噪比）、UCIQE（彩色图像质量评价指标）和UIQM（统一图像质量度量）等图像质量评估指标，以定量比较增强前后图像的效果，并深入分析不同模型在不同场景下的性能表现。 此外，为提升水下视觉增强系统的实用性和稳定性，本文还从模型优化、多模态融合以及硬件选择等方面提供了切实可行的建议，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。
URL
https://arxiv.org/abs/2508.17397
