Multi-focus image fusion and super-resolution with convolutional neural network

Abstract

The aim of multi-focus image fusion is to create a synthetic all-in-focus image from several images each of which is obtained with different focus settings. However, if the resolution of source images is low, the fused images with traditional fusion method would be also in low-quality, which hinders further image analysis even the fused image is all-in-focus. This paper presents a novel joint multi-focus image fusion and super-resolution method via convolutional neural network (CNN). The first level network features of different source images are fused with the guidance of the local clarity calculated from the source images. The final high-resolution fused image is obtained with the reconstruction network filters which act like averaging filters. The experimental results demonstrate that the proposed approach can generate the fused images with better visual quality and acceptable computation efficiency as compared to other state-of-the-art works.