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Related Experiment Video

Updated: Sep 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Mamba-convolution hybrid network for underwater image enhancement.

Hailan Chen¹, Yijian Wang^2,3, Lihua Wu⁴

¹School of Science, Jimei University, Xiamen, 361021, China.

Scientific Reports

|August 30, 2025

Summary

This study introduces a Mamba-Convolution network for underwater image enhancement (MC-UIE), improving clarity and color accuracy. The novel method enhances marine ecological monitoring and underwater target detection.

Keywords:

Underwater image enhancement disentanglement strategy multiscale feature fusion underwater optical imaging

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Area of Science:

Computer Vision
Marine Biology
Image Processing

Background:

Underwater images suffer from low clarity and color distortion due to marine conditions and lighting.
Degraded image quality hinders marine ecological monitoring and underwater target detection.

Purpose of the Study:

To develop an effective method for underwater image enhancement.
To improve the quality of underwater imagery for scientific applications.

Main Methods:

A Mamba-Convolution network for Underwater Image Enhancement (MC-UIE) was developed.
Utilized standard convolution, Mamba-Convolution Hybrid Blocks (M-C HB) with 2D Selective Scan (SS2D) and Feature Attention Module (FAM), and Cross Fusion Mamba Blocks (CFMB).

Main Results:

The MC-UIE method significantly enhances global and local image dependencies.
Achieved superior performance in color, illumination, and detail restoration compared to existing methods.
Demonstrated effectiveness through extensive qualitative and quantitative experiments on mainstream datasets.

Conclusions:

The proposed MC-UIE method offers a significant advancement in underwater image enhancement.
The approach effectively addresses the challenges of poor image quality in marine environments.
The developed network shows promise for improving marine ecological monitoring and underwater target identification.