霜冰优化算法与多尺度信息融合的水下图像增强OA

To address the issues of color distortion and severe loss of details in underwater images,an underwater image enhancement method based on the rime optimization algorithm(RIME)and multi-scale information fusion is proposed.Firstly,to tackle the problem of color distortion,an innovative adaptive gamma correction red channel compensation framework is introduced,which integrates the Dark Channel Prior(DCP)algorithm and is optimized using RIME,ensuring natural color restoration for underwater images.Secondly,the Contrast Limited Adaptive Histogram Equalization(CLAHE)method is applied to enhance image contrast.Finally,the enhanced results from the two channels are processed through a four-weight Gaussian-Laplacian pyramid and fused at multiple scales,ultimately producing images of superior visual quality.Experimental results show that the average values of UIQM,UCIQE,and SSIM achieved by this algorithm reach 4.598,0.633,and 0.726,respectively.Additionally,comparative experiments conducted on the UIEB,RUIE,and EUVP datasets demonstrate that the proposed method significantly outperforms other comparison algorithms,confirming its superior effectiveness in improving the quality of underwater images.