论文标题
Real-MFF：一个大型现实的多重点图像数据集，具有地面真相
Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth
论文作者
论文摘要
多聚焦图像融合是一种从两个或多个部分专注的源图像生成全焦点图像的技术，可以使许多计算机视觉任务受益。但是，目前尚无大型且现实的数据集来执行多聚焦图像融合中算法的令人信服的评估和比较。此外，在没有合适数据集的情况下，很难训练深层神经网络进行多聚焦图像融合。在这封信中，我们介绍了一个称为RealMFF的大型逼真的多重点数据集，其中包含710对带有相应地面真相图像的源图像。数据集由光场图像生成，源图像和地面真相图像都是现实的。为了作为现有多聚焦图像融合算法的完善基准，也可以作为用于未来基于深度学习方法的未来开发的适当培训数据集，该数据集包含各种场景，包括建筑物，植物，植物，人类，购物中心，购物中心，正方形等。我们还评估了该数据集上的10种典型的多对焦算法，以进行插图。
Multi-focus image fusion, a technique to generate an all-in-focus image from two or more partially-focused source images, can benefit many computer vision tasks. However, currently there is no large and realistic dataset to perform convincing evaluation and comparison of algorithms in multi-focus image fusion. Moreover, it is difficult to train a deep neural network for multi-focus image fusion without a suitable dataset. In this letter, we introduce a large and realistic multi-focus dataset called Real-MFF, which contains 710 pairs of source images with corresponding ground truth images. The dataset is generated by light field images, and both the source images and the ground truth images are realistic. To serve as both a well-established benchmark for existing multi-focus image fusion algorithms and an appropriate training dataset for future development of deep-learning-based methods, the dataset contains a variety of scenes, including buildings, plants, humans, shopping malls, squares and so on. We also evaluate 10 typical multi-focus algorithms on this dataset for the purpose of illustration.
