Multi-Focus Image Fusion Based on Generative Adversarial Network

Multi-focus image fusion can fuse a series of images that have a different focus in the same scene. To overcome the disadvantage extraction of the blurring character in multi-focus images, a generative adversarial network model based on U-Net is proposed. Firstly, the generator uses U-Net and SSE to extract the feature of the multi-focus image, and fuses images. Then, the discriminator uses convolutional layers to distinguish the fused result between the existed and the generative. Furthermore, a loss function has the loss of adversarial in the generator, loss of mapping, loss of gradient, loss mean square error and the loss of adversarial in the discriminator. The train data of generative adversarial network uses the dataset of Pascal VOC2012 to generate and includes near-focus image, far-focus image, mapping image and all-in-focus image. The experimental result shows that the proposed generative adversarial network model can effectively extract the blurring feature in multi-focus images, and the fused image have good performances on mutual information, phase congruency and structural similarity. © 2021, Beijing China Science Journal Publishing Co. Ltd. All right reserved.