Stage-wise multi-focus fusion for numerous misaligned industrial images

Multi-focus image fusion is a technique that combines information from multiple images to generate a single composite image that retains all the essential details and features of the original images. Traditional methods can achieve rapid fusion but struggle with image misalignment. Due to the strong expressive power of deep neural networks, deep learning methods can handle misaligned situations but face challenges when fusing many images. In this work, a stage-wise multi-focus fusion method is proposed. Firstly, in the coarse fusion stage, the number of images for subsequent fusion is reduced by fusing similar images. Here, several consecutive images are sequentially fused by using an indicator to distinguish between clear and blurred areas and then merging the clear regions. Subsequently, in the fine fusion stage, pairwise merging is adopted to obtain the final result. For each pair of sub-images, a blur filter, difference operator, and guided filter are utilized to create a decision map, followed by pixel-wise weighted averaging to fuse the source images. The proposed method is validated using two multi-focus image datasets with numerous misaligned industrial images, namely the Lampwick dataset and the Impurity dataset. Moreover, its generalization ability is demonstrated by conducting experiments on the Lytro and MFFW datasets. Our findings show that, despite the industrial images often possessing the characteristics of being numerous and misaligned, our method not only achieves fast fusion but also yields the best results.