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

TL;DR

This paper compares VGG19 and ResNet50 deep learning models for underwater image enhancement, proposing a unified approach that leverages their strengths to improve image quality in complex underwater scenes.

Contribution

It introduces a novel multi-model fusion method combining VGG19 and ResNet50 for enhanced underwater image quality, with comprehensive evaluation metrics and practical system suggestions.

Findings

ResNet50 outperforms VGG19 in certain scenarios

Multi-model fusion improves enhancement quality

Quantitative metrics confirm effectiveness

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 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.