Underwater Image Enhancement Based on Multi-Scale Fusion and Detail Sharpening

To address the issues of color cast, insufficient contrast, and detail loss in underwater optical images, this paper proposes an underwater image enhancement method based on multi-scale fusion and detail sharpening. The algorithm first applies an improved Gray World White Balance method with color compensation to perform color correction on the original underwater image. Subsequently, two processed images are generated for fusion: the first image is obtained by applying a Particle Swarm Optimization-enhanced Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm to the color-corrected image to enhance contrast; the second image is produced by applying an adaptive gamma correction algorithm to improve uneven illumination regions. These two images are then fused using a multi-scale fusion strategy. Finally, a weighted multi-scale detail sharpening technique is employed to further enhance the texture details of the fused image, yielding the final enhanced result. The performance of the proposed method is evaluated using no-reference underwater image quality metrics: the Underwater Image Quality Measure (UIQM) and the Patch-based Contrast Quality Index (PCQI), and tested on the open-source dataset from Nanyang Technological University. Experimental results demonstrate that the proposed method leads to an improvement in underwater image quality in both qualitative and quantitative assessments.