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DOI码：10.1016/j.neucom.2021.10.115

发表刊物：Neurocomputing

摘要：The general aim of multi-focus image fusion is to gather focused regions of different images to generate a unique all-in-focus fused image. Deep learning based methods become the mainstream of image fusion by virtue of its powerful feature representation ability. However, most of the existing deep learning structures failed to balance fusion quality and end-to-end implementation convenience. End-to-end decoder design often leads to unrealistic result because of its non-linear mapping mechanism. On the other hand, generating an intermediate decision map achieves better quality for the fused image, but relies on the rectification with empirical post-processing parameter choices. In this work, to handle the requirements of both output image quality and comprehensive simplicity of structure implementation, we propose a cascade network to simultaneously generate decision map and fused result with an end-to-end training procedure. It avoids the dependence on empirical post-processing methods in the inference stag. To improve the fusion quality, we introduce a gradient aware loss function to preserve gradient information in output fused image. In addition, we design a decision calibration strategy to decrease the time consumption in the application of multiple images fusion. Extensive experiments are conducted to compare with 19 different state-of-the-art multi-focus image fusion structures with 6 assessment metrics. The results prove that our designed structure can generally ameliorate the output fused image quality, while implementation efficiency increases over 30% for multiple images fusion.

合写作者：Xiang Yin,Di Wu,Haokai Shen,Xiaojuan Ban,Yu Wang

第一作者：Boyuan Ma

论文类型：期刊论文

学科门类：工学

文献类型：J

卷号：470

期号：1

页面范围：204-216. (TOP期刊)

是否译文：否

发表时间：2022-01-01

收录刊物：SCI