Zhang Xingchen
IEEE Trans Pattern Anal Mach Intell. 2022 Sep;44(9):4819-4838. doi: 10.1109/TPAMI.2021.3078906. Epub 2022 Aug 4.
Multi-focus image fusion (MFIF) is an important area in image processing. Since 2017, deep learning has been introduced to the field of MFIF and various methods have been proposed. However, there is a lack of survey papers that discuss deep learning-based MFIF methods in detail. In this study, we fill this gap by giving a detailed survey on deep learning-based MFIF algorithms, including methods, datasets and evaluation metrics. To the best of our knowledge, this is the first survey paper that focuses on deep learning-based approaches in the field of MFIF. Besides, extensive experiments have been conducted to compare the performance of deep learning-based MFIF algorithms with conventional MFIF approaches. By analyzing qualitative and quantitative results, we give some observations on the current status of MFIF and discuss some future prospects of this field.
多聚焦图像融合(MFIF)是图像处理中的一个重要领域。自2017年以来,深度学习已被引入到MFIF领域,并提出了各种方法。然而,缺乏详细讨论基于深度学习的MFIF方法的综述论文。在本研究中,我们通过对基于深度学习的MFIF算法进行详细综述来填补这一空白,包括方法、数据集和评估指标。据我们所知,这是第一篇专注于MFIF领域中基于深度学习方法的综述论文。此外,还进行了广泛的实验,以比较基于深度学习的MFIF算法与传统MFIF方法的性能。通过分析定性和定量结果,我们对MFIF的现状给出了一些观察,并讨论了该领域的一些未来前景。