Optimizing multi-focus image fusion through convolutional attention vision transformers and spatial consistency models

Multi-Focus Image Fusion (MFIF) aims to integrate all pixels in the images to avoid defocused pixels simultaneously. The removal of defocused pixels in the image is more challenging because the traditional approach makes it difficult to accurately detect defocused regions in the image. In this paper, the Convolutional Attention based Vision Transformer based Iterative Multi-Scale Fusion Network (CAViT-IMSFN) model is proposed for MFIF. The collected input images are fed into preprocessing approaches like normalization, data imputation, and augmentation for improving the generalization ability of the model and also for reducing overfitting issues. The convolutional-based model named MobileNetV2 is used to extract local features and the AViT model is introduced to extract global features present in the preprocessed images. The spatial attention model is used in the integration stage to integrate both local and global features that preserve spatial consistency. For enhancing image quality and spatial consistency, the iterative refinement model is implemented according to the feedback mechanism that helps to update the fused output iteratively. The gradient boosting optimization algorithm is used for weight adjustment and the multi-scale fusion model is applied for the identification of focused and defocused portions in the images. This proposed model improves network adaptability by capturing multi-scale features and also has the ability to handle various levels of detail and complexity. The experimental evaluation is performed in terms of using diverse performance evaluation measures and quantitative analyses. The proposed model attained performances of 1.435 from Normalized Mutual Information (NMI) and 0.78 s from computational time. The result showed that the proposed model achieved superior outcomes rather than other MFIF-related existing approaches.