Multi-focus image fusion with the all convolutional neural network

• 来源期刊: 光电子快报（英文版）
• 年，卷（期）: 2018,（1）
• 页码范围: 71-75
• 页数: 5页
• 语种: 英文
• DOI: 10.1007/s11801-018-7207-x

摘要

A decision map contains complete and clear information about the image to be fused,which is crucial to various image fusion issues,especially multi-focus image fusion.However,in order to get a satisfactory image fusion effect,getting a decision map is very necessary and usually difficult to finish.In this letter,we address this problem with convolutional neural network (CNN),aiming to get a state-of-the-art decision map.The main idea is that the max-pooling of CNN is replaced by a convolution layer,the residuals are propagated backwards by gradient descent,and the training parameters of the individual layers of the CNN are updated layer by layer.Based on this,we propose a new all CNN (ACNN)-based multi-focus image fusion method in spatial domain.We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results.Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.