A novel split-frequency feature fusion framework for processing the dual-optical images of offshore oil spills

This paper presents a novel split-frequency feature fusion framework used for processing the dual-optical (infrared-visible) images of offshore oil spills. The self-coding network is used for high-frequency features of oil spill images based on local cross-stage residual dense blocks to achieve feature extraction and construct a regularized fusion strategy. The adaptive weights are designed to increase the proportion of high-frequency features in source images during the low-frequency feature fusion process. A global residual branch is established to reduce the loss of oil spill texture features. The network structure of the primary residual dense block auto-encoding network is optimized based on the local cross-stage method to further reduce the network parameters and improve the network operation speed. To verify the effectiveness of the proposed infrared-visible image fusion algorithm, the BiSeNetV2 algorithm is selected as the oil spill detection algorithm to realize the pixel accuracy of the oil spill image features at 91%.

Automated summary

This paper presents a novel split-frequency feature fusion framework for processing dual-optical images of offshore oil spills. The framework utilizes a self-coding network based on local cross-stage residual dense blocks for feature extraction and constructs a regularized fusion strategy. Adaptive weights are designed to increase the proportion of high-frequency features during the fusion process. A global residual branch is established to preserve oil spill texture features. The network structure is optimized to reduce parameters and improve operation speed.