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Underwater image enhancement using Divide-and-Conquer network.

Shijian Zheng^1,2, Rujing Wang^2,3, Guo Chen¹

¹Department of Information Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, China.

|March 5, 2024

Summary

This study introduces a novel Divide-and-Conquer network (DC-net) for enhancing underwater images. The DC-net effectively addresses color distortion and blur, improving visual quality and information extraction from underwater scenes.

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Underwater images suffer from color distortion and blur due to light attenuation and water conditions.
Existing underwater image enhancement methods often struggle with comprehensive quality improvement.

Purpose of the Study:

To develop an effective method for enhancing underwater images.
To improve both the visual quality and the information extractability of underwater imagery.

Main Methods:

A novel Divide-and-Conquer network (DC-net) was designed.
The DC-net integrates a texture network with multi-axis attention, a color network using adaptive 3D look-up tables, and a refinement network.

Main Results:

The proposed DC-net achieved superior visual quality in underwater images.
Qualitative and quantitative performance surpassed state-of-the-art underwater image enhancement methods.

Conclusions:

The DC-net offers a robust solution for underwater image enhancement.
The method demonstrates significant improvements in handling complex underwater image degradations.