Exploring and monitoring underwater environments pose unique challenges due to water’s complex optical properties, which significantly impact image quality. Challenges like light absorption and scattering result in color distortion and decreased visibility. Traditional underwater image acquisition methods face these obstacles, highlighting the need for advanced techniques to solve the image color shift and image detail loss caused by the underwater environment in the image enhancement process. This study proposes a salient region-guided underwater image enhancement fusion method to alleviate these problems. First, this study proposes an advanced dark channel prior method to reduce haze effects in underwater images, significantly improving visibility and detail. Subsequently, a comprehensive RGB color correction restores the underwater scene’s natural appearance. The innovation of our method is that it fuses through a combination of Laplacian and Gaussian pyramids, guided by salient region coefficients, thus preserving and accentuating the visually significant elements of the underwater environment. Comprehensive subjective and objective evaluations demonstrate our method’s superior performance in enhancing contrast, color depth, and overall visual quality compared to existing methods.

This paper presents a novel underwater image enhancement method addressing the challenges of low contrast, color distortion, and detail loss prevalent in underwater photography. Unlike existing methods that may introduce color bias or blur during enhancement, our approach leverages a two-pronged strategy. First, an Efficient Fusion Edge Detection (EFED) module preserves crucial edge information, ensuring detail clarity even in challenging turbidity and illumination conditions. Second, a Multi-scale Color Parallel Frequency-division Attention (MCPFA) module integrates multi-color space data with edge information. This module dynamically weights features based on their frequency domain positions, prioritizing high-frequency details and areas affected by light attenuation. Our method further incorporates a dual multi-color space structural loss function, optimizing the performance of the network across RGB, Lab, and HSV color spaces. This approach enhances structural alignment and minimizes color distortion, edge artifacts, and detail loss often observed in existing techniques. Comprehensive quantitative and qualitative evaluations using both full-reference and no-reference image quality metrics demonstrate that our proposed method effectively suppresses scattering noise, corrects color deviations, and significantly enhances image details. In terms of objective evaluation metrics, our method achieves the best performance in the test dataset of EUVP with a PSNR of 23.45, SSIM of 0.821, and UIQM of 3.211, indicating that it outperforms state-of-the-art methods in improving image quality.