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Multi-Domain Rapid Enhancement Networks for Underwater Images.

Longgang Zhao¹, Seok-Won Lee^1,2

¹The Knowledge-Intensive Software Engineering (NiSE) Research Group, Department of Artificial Intelligence, Ajou University, Suwon City 16499, Republic of Korea.

Sensors (Basel, Switzerland)

|November 14, 2023

Summary

This summary is machine-generated.

This study introduces a multichannel deep convolutional neural network (MDCNN) for superior underwater image enhancement. The model improves domain adaptation and image quality, outperforming existing methods.

Keywords:

DCNN domain adaptability multi-domain machine learning perceptual loss underwater image enhancement

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Area of Science:

Marine Engineering
Computer Vision
Deep Learning

Background:

Underwater images from marine operations often exhibit color distortion and low contrast.
Existing deep learning models struggle with multi-source data from diverse perspectives.

Purpose of the Study:

To develop an advanced deep learning model for multi-source underwater image enhancement.
To improve domain adaptation and enhance image quality for marine engineering applications.

Main Methods:

Proposed a multichannel deep convolutional neural network (MDCNN) integrated with VGG architecture.
Implemented separate data channels for different domains and linked VGG parameters for improved domain adaptation.
Introduced novel loss functions: multi-domain image perception, multilabel soft edge, pixel-level, and external monitoring loss.

Main Results:

The proposed MDCNN model demonstrated superior performance in underwater image enhancement.
Achieved better structural and textural similarity in enhanced images compared to state-of-the-art methods.
Showcased an average performance increase of 0.11 on the UIQM metric across different datasets.

Conclusions:

The MDCNN model effectively addresses color distortion and low contrast in underwater images.
The proposed loss functions significantly enhance image quality and structural similarity.
The model offers a robust solution for multi-source underwater image enhancement in marine engineering.