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Updated: May 10, 2025

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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Published on: April 18, 2025

CASF-Net: Underwater Image Enhancement with Color Correction and Spatial Fusion.

Kai Chen¹, Zhenhao Li¹, Fanting Zhou¹

¹Key Laboratory of Ocean Observation and Information of Hainan Province, Sanya Oceanographic Institution, Ocean University of China, Sanya 572000, China.

Sensors (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

This study introduces CASF-Net, a novel underwater image enhancement (UIE) method addressing contrast and texture issues. The new approach, along with a diverse dataset, significantly improves UIE performance compared to existing techniques.

Keywords:

channel adaptive factor multi-scale fusion spatial information fusion underwater image enhancement

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Area of Science:

Marine Biology
Computer Vision
Image Processing

Background:

Underwater images are vital for marine resource exploration but suffer from low contrast and distorted textures.
Existing underwater image enhancement (UIE) methods often overlook these specific challenges, limiting their effectiveness.

Purpose of the Study:

To develop a novel UIE method that effectively addresses insufficient contrast and surface texture distortion.
To introduce a comprehensive dataset for training and evaluating UIE algorithms.

Main Methods:

Proposed CASF-Net (channel-adaptive and spatial-fusion Net) incorporating a channel-adaptive correction module (CACM) for feature extraction and color correction.
Implemented a spatial multi-scale fusion module (SMFM) to mitigate surface texture distortion and enhance saturation.
Introduced the Large-scale High-resolution Underwater Image Enhancement Dataset (LHUI) with 13,080 image pairs.

Main Results:

CASF-Net demonstrated superior performance in enhancing underwater images compared to existing methods.
The CACM module effectively improved contrast and color correction.
The SMFM module successfully reduced surface texture distortion and boosted image saturation.

Conclusions:

CASF-Net offers a significant advancement in UIE, effectively tackling contrast and texture issues.
The LHUI dataset provides a valuable resource for future research in underwater image enhancement.