视觉任务友好的水下图像增强方法OA

Underwater images suffer from severe color and structural distortions,which degrade the performance of various underwater vision tasks.Existing underwater image enhancement methods focus on improving visual appearance while ignoring the necessity of optimizing the downstream vision tasks.To address this issue,this paper proposed an underwater image enhancement method namely visual task-friendly network(VTF-Net).Specifically,it first designed a novel spatial-frequency fusion(SFF)module,which could significantly improve the model's perception of texture details and image fidelity.Second,to achieve efficient information transmission between the encoder and decoder,it introduced a multi-scale cross-attention(MSCA)module and a bottleneck attention(BNA)module,which enhanced the perception of global gradients while ensuring efficient feature extraction,thereby effectively alleviating color cast and blurring.Finally,in line with the concept of visual task friendliness,it proposed a detection loss function that guided the optimization direction of the model by incorporating underwater object detection results.Experimental results demonstrate that the proposed method achieves superior performance in both qualitative and quantitative evaluations and obtains the best result in the application experiment of underwater object detection.