SESF-Fuse: An Unsupervised Deep Model for Multi-Focus Image Fusion
This addresses the problem of combining multiple images with different focus regions into a single all-in-focus image for applications like photography and computer vision, representing an incremental improvement over prior methods.
The authors tackled the multi-focus image fusion problem by proposing an unsupervised deep learning model that analyzes sharp appearance in deep features rather than original images, achieving state-of-the-art performance compared to 16 existing methods in objective and subjective assessments.
In this work, we propose a novel unsupervised deep learning model to address multi-focus image fusion problem. First, we train an encoder-decoder network in unsupervised manner to acquire deep feature of input images. And then we utilize these features and spatial frequency to measure activity level and decision map. Finally, we apply some consistency verification methods to adjust the decision map and draw out fused result. The key point behind of proposed method is that only the objects within the depth-of-field (DOF) have sharp appearance in the photograph while other objects are likely to be blurred. In contrast to previous works, our method analyzes sharp appearance in deep feature instead of original image. Experimental results demonstrate that the proposed method achieves the state-of-art fusion performance compared to existing 16 fusion methods in objective and subjective assessment.