To address the inefficiency of target detection in Autonomous Underwater Vehicles,a Retinex-based underwater image enhancement fusion algorithm is proposed.The algorithm focuses on mitigating color deviation and image blurring caused by varying light intensity in underwater environments.Firstly,the red and dark channel a priori theories are utilized for image defogging.Next,red channel filtering addresses the issue of insufficient red light underwater.Adaptive multi-scale retinal enhancement is combined with color recovery algorithms,using Laplace sharpening and color recovery factors for effective color correction.The proposed algorithm is validated through experiments and comprehensive analyses,both subjective and objective.Results demonstrate a 35.56%improvement in the underwater color image quality assessment index compared to the adaptive multi-scale retina enhancement algorithm,and a 19.03%increase in information entropy compared to the original image.The method significantly reduces blurriness and corrects color imbalance in underwater images,proving its effectiveness and practicality.Furthermore,when integrated with the YOLO framework,the algorithm shows strong potential for underwater target recognition.
维普
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