Real-MFF Dataset: A Large Realistic Multi-focus Image Dataset with Ground Truth
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Multi-focus image fusion, a technique to generate an all-in-focus image from two or more source images, can benefit many computer vision tasks. However, currently there is no large and realistic dataset to perform convincing evaluation and comparison for exiting multi-focus image fusion. For deep learning methods, it is difficult to train a network without a suitable dataset. In this paper, we introduce a large and realistic multi-focus dataset containing 800 pairs of source images with the corresponding ground truth images. The dataset is generated using a light field camera, consequently, the source images as well as the ground truth images are realistic. Moreover, the dataset contains a variety of scenes, including buildings, plants, humans, shopping malls, squares and so on, to serve as a well-founded benchmark for multi-focus image fusion tasks. For illustration, we evaluate 10 typical multi-focus algorithms on this dataset.
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