计算机科学
人工智能
深度学习
图像融合
光学(聚焦)
模式识别(心理学)
图像(数学)
计算机视觉
机器学习
光学
物理
标识
DOI:10.1109/tpami.2021.3078906
摘要
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 categories, methods, datasets and evaluation metrics. To the best of our knowledge, this is the first survey paper which focuses on deep learning based approaches in the field of MFIF. Besides, extensive experiments have been conducted to compare the performances 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.
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