# SESF-Fuse: An Unsupervised Deep Model for Multi-Focus Image Fusion

arXiv: https://arxiv.org/abs/1908.01703 · HTML: https://mlsanity.com/paper/1908.01703/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2019.

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

- [TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning](https://mlsanity.com/paper/2112.01030/) (2021)
- [Generation and Recombination for Multifocus Image Fusion with Free Number of Inputs](https://mlsanity.com/paper/2309.04657/) (2023)
- [Test-Time Dynamic Image Fusion](https://mlsanity.com/paper/2411.02840/) (2024)
- [UFA-FUSE: A novel deep supervised and hybrid model for multi-focus image fusion](https://mlsanity.com/paper/2101.04506/) (2021)
- [TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning](https://mlsanity.com/paper/2201.07451/) (2022)
- [Generating Robust Adversarial Examples against Online Social Networks (OSNs)](https://mlsanity.com/paper/2310.12708/) (2023)
- [Towards Real-World Focus Stacking with Deep Learning](https://mlsanity.com/paper/2311.17846/) (2023)
- [Single image deep defocus estimation and its applications](https://mlsanity.com/paper/2107.14443/) (2021)
- [Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus Imaging](https://mlsanity.com/paper/2110.05706/) (2021)
- [Addressing the Depth-of-Field Constraint: A New Paradigm for High Resolution Multi-Focus Image Fusion](https://mlsanity.com/paper/2510.19581/) (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)

In this work, we propose a novel unsupervised deep learning model to address multi-focus image fusion problem. First, we train an encoder-decoder network in unsupervised manner to acquire deep feature of input images. And then we utilize these features and spatial frequency to measure activity level and decision map. Finally, we apply some consistency verification methods to adjust the decision map and draw out fused result. The key point behind of proposed method is that only the objects within the depth-of-field (DOF) have sharp appearance in the photograph while other objects are likely to be blurred. In contrast to previous works, our method analyzes sharp appearance in deep feature instead of original image. Experimental results demonstrate that the proposed method achieves the state-of-art fusion performance compared to existing 16 fusion methods in objective and subjective assessment.