🤖 AI Summary
To address severe image degradation—including color distortion, low contrast, and blur—in complex underwater scenes, this paper proposes a multi-scale feature collaborative enhancement model integrating VGG19 and ResNet50. The method jointly optimizes structural detail preservation and accurate color restoration via complementary deep feature extraction within a unified end-to-end training framework. A novel cross-network feature fusion mechanism is introduced to synergistically leverage hierarchical representations from both backbones. Comprehensive quantitative evaluation is conducted using multiple objective metrics—PSNR, UCIQE, and UIQM—on diverse real-world underwater datasets. Experimental results demonstrate that the proposed approach significantly outperforms conventional enhancement methods and single-backbone baselines: it achieves an average UIQM improvement of 12.7%, exhibits strong robustness across heterogeneous underwater conditions, and maintains computational efficiency suitable for practical deployment.
📝 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.