论文标题
MFFW：一个用于多重点图像融合的新数据集
MFFW: A new dataset for multi-focus image fusion
论文作者
论文摘要
多聚焦图像融合（MFF）是计算摄影领域的基本任务。当前的方法已取得了重大的性能提高。发现在模拟图像集或Lytro数据集上评估当前方法。最近，越来越多的研究人员关注Defocus扩散效应，这是现实世界中多聚焦图像的现象。尽管如此，在模拟或lytro数据集中，散焦传播效应并不明显，在模拟或lytro的数据集中，流行方法的性能非常相似。为了比较其在图像上具有散焦效果的性能，本文构建了一个新的数据集在野外（MFFW）。它包含在互联网上收集的19对多聚焦图像。我们注册了所有对源图像的对，并为一部分对提供了焦点图和参考图像。与Lytro数据集相比，MFFW中的图像显着遭受散焦分布效应的影响。此外，MFFW的场景更为复杂。该实验表明，MFFW数据集上的大多数最新方法无法牢固地产生令人满意的融合图像。 MFFW可以是一个新的基线数据集，用于测试MMF算法是否能够处理散焦分布效应。
Multi-focus image fusion (MFF) is a fundamental task in the field of computational photography. Current methods have achieved significant performance improvement. It is found that current methods are evaluated on simulated image sets or Lytro dataset. Recently, a growing number of researchers pay attention to defocus spread effect, a phenomenon of real-world multi-focus images. Nonetheless, defocus spread effect is not obvious in simulated or Lytro datasets, where popular methods perform very similar. To compare their performance on images with defocus spread effect, this paper constructs a new dataset called MFF in the wild (MFFW). It contains 19 pairs of multi-focus images collected on the Internet. We register all pairs of source images, and provide focus maps and reference images for part of pairs. Compared with Lytro dataset, images in MFFW significantly suffer from defocus spread effect. In addition, the scenes of MFFW are more complex. The experiments demonstrate that most state-of-the-art methods on MFFW dataset cannot robustly generate satisfactory fusion images. MFFW can be a new baseline dataset to test whether an MMF algorithm is able to deal with defocus spread effect.
