# MFFW: A new dataset for multi-focus image fusion

arXiv: https://arxiv.org/abs/2002.04780 · HTML: https://mlsanity.com/paper/2002.04780/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

## Built on by (most cited first, 13 of 13)

- [Efficient Rectified Flow for Image Fusion](https://mlsanity.com/paper/2509.16549/) (2025)
- [Mutual-Guided Dynamic Network for Image Fusion](https://mlsanity.com/paper/2308.12538/) (2023)
- [A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking](https://mlsanity.com/paper/2505.19858/) (2025)
- [Generation and Recombination for Multifocus Image Fusion with Free Number of Inputs](https://mlsanity.com/paper/2309.04657/) (2023)
- [UniFusion: A Unified Image Fusion Framework with Robust Representation and Source-Aware Preservation](https://mlsanity.com/paper/2603.14214/) (2026)
- [Task-Generalized Adaptive Cross-Domain Learning for Multimodal Image Fusion](https://mlsanity.com/paper/2508.15505/) (2025)
- [Towards Real-World Focus Stacking with Deep Learning](https://mlsanity.com/paper/2311.17846/) (2023)
- [RLBayes: a Bayesian Network Structure Learning Algorithm via Reinforcement Learning-Based Search Strategy](https://mlsanity.com/paper/2504.05167/) (2025)
- [Quaternion Sparse Decomposition for Multi-focus Color Image Fusion](https://mlsanity.com/paper/2505.02365/) (2025)
- [Gradient-based multi-focus image fusion with focus-aware saliency enhancement](https://mlsanity.com/paper/2509.22392/) (2025)
- [Inter-Image Pixel Shuffling for Multi-focus Image Fusion](https://mlsanity.com/paper/2603.07120/) (2026)
- [UHD-MFF: Shattering Barriers in Multi-Focus Ultra-High-Definition Image Fusion via Learnable Lookup Tables](https://mlsanity.com/paper/2606.31242/) (2026)
- [Clarity Contrast and Similarity Selection for Multi-Focus Image Fusion](https://mlsanity.com/paper/2608.03252/) (2026)

## Abstract (arXiv)

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.