Multi-focus image fusion method using energy of Laplacian and convolutional neural network

Abstract:

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.