The aim of multi-focus image fusion technology is to produce an all-in-focus image in which the clear parts of different source images are integrated to a single image. Most of the existing fusion methods still suffer from the problems such as block artifacts

artificial edges

halo effects

ringing effects

and contrast reduction. To address these problems

a multi-focus image fusion method using the energy of Laplacian CNN is proposed in this paper. The focus information of source images can be extracted effectively by using Laplacian energy operator

and the focus feature extracted from focus information maps by the trained convolutional neural network model can effectively distinguish focused sub-blocks from defocused sub-blocks. The trained convolutional neural network model not only has a good ability to extract the relative focus degree of active windows

but also can obtain an accurate segmentation boundary. After multiple rounds of training

the convolutional neural network model can well 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

the 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 visual effects and quantitative evaluation.