Multi-focus image fusion (MFIF) is an image enhancement method that combines the focused regions from different source images to form a fully sharp image. Currently, in the context of MFIF methods, there are two main challenges. First, traditional methods such as spatial domain approaches produce fusion images with high objective scores, but they suffer from strong defocus spread effects (DSE) and artifacts at the fusion boundaries. Second, deep learning methods lack a dataset generated from plenoptic cameras and require extensive manual parameter tuning, resulting in time-consuming training processes. To address these challenges, this paper proposes an unsupervised multi-focus image fusion method based on target image prior information. Firstly, the internal prior information of the source image itself and the external prior information of the initial fusion image generated by a spatial domain method are utilized as inputs for the G-Net and F-Net networks, respectively, both the G-Net and F-Net networks are components of the UNet-based Deep Image Prior (DIP) network; then, a reference mask generated by a spatial domain method is introduced to assist G-Net network for generating a guiding decision map; finally, the decision map and the initial fusion image are used to jointly optimize the F-Net network, producing the final fusion image. The validation experiments are conducted on the Lytro dataset with real reference images and the MFFW dataset with strong DSE exhibiting in the fusion boundaries, and employ five widely used objective metrics for performance evaluation. The experimental results demonstrate that the proposed method significantly reduces the number of optimization iterations, and outperforms eight state-of-the-art MFIF approaches in terms of the subjective and objective performance evaluation, and especially shows superior performance on the datasets with strong DSE exhibiting in the fusion boundaries.

[2024-01-26] Accepted Paper /doc/2023.09.0444/v1