W-EICMFusion: A fusion network for infrared and visible images utilising WOA hyperparameter optimisation

Abstract

Currently, most innovations in image fusion methods focus on designing network architectures for source image feature extraction and formulating loss functions for fusion networks, while often neglecting the necessary adjustments and optimisations of hyperparameters within these loss functions. However, the selection of hyperparameters for the loss function of the fusion network is crucial because it determines the iterative direction of the network and significantly influences the final results. This study proposes an image fusion network with hyperparameter adaptive optimisation adjustment, termed whale optimisation algorithm-edge invertible CMamba fusion (W-EICMFusion). First, we introduce a CMamba module that effectively extracts common information features within the local and broad receptive fields. We designed an edge-extraction invertible neural network (EE-INN) module that captures edge detail information from two modalities, and a fusion layer known as a residual dense efficient channel attention network (RDENet) to enhance the extraction of complementary information. Unlike other fusion networks that depend on manual parameter tuning, this study employs the whale optimisation algorithm (WOA) to determine the optimal hyperparameters adaptively. Our experiments compared our designed fusion network with 11 recently developed advanced methods related to it. The proposed method demonstrates the best overall performance and achieves the highest comprehensive score in comparative experiments conducted on the MSRS, LLVIP, Road-Scene, and M3FD datasets. Furthermore, it attained the highest detection accuracy in subsequent tasks, such as object detection. In the final design of the optimisation algorithm generalisation experiments, the proposed approach to hyperparameter adaptive optimisation produced superior classification outcomes. The code can be found at https://github.com/ljcuestc/W-EICMFusion.git.