Multifocus Image Fusion Method Based on Convolutional Deep Belief Network

Abstract

Multifocus image fusion is a technique that can integrate the focus information of different source images into a single composite image. At present, most fusion methods still suffer from problems such as block artifacts, artificial edges, halo effects, ringing effects, and contrast reduction. To address these problems, a novel multifocus image fusion method based on a convolutional deep belief network is proposed in this paper. The convolutional operator can effectively extract the focus information of source images, and the focused features extracted from source images can effectively distinguish focused windows from defocused windows. After multiple rounds of training, the convolutional deep belief network model can establish an effective mapping between source images and a score map, which is essential to generate an accurate focus map. Then, the focus map is further modified using binary segmentation and small region filtering, and the final decision map for fusion is obtained. Finally, according to the weights provided by the final decision map, a final fusion image will be formed by fusing multiple source images. The experimental results show that the proposed method is superior to other existing fusion methods in terms of subjective visual effects and objective quantitative evaluation.