# Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy

arXiv: https://arxiv.org/abs/2012.14678 · HTML: https://mlsanity.com/paper/2012.14678/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [Image Fusion via Vision-Language Model](https://mlsanity.com/paper/2402.02235/) (2024)
- [Task-driven Image Fusion with Learnable Fusion Loss](https://mlsanity.com/paper/2412.03240/) (2024)
- [Generation and Recombination for Multifocus Image Fusion with Free Number of Inputs](https://mlsanity.com/paper/2309.04657/) (2023)
- [Deep Unfolding Multi-modal Image Fusion Network via Attribution Analysis](https://mlsanity.com/paper/2502.01467/) (2025)
- [Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network](https://mlsanity.com/paper/2210.08181/) (2022)
- [BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring](https://mlsanity.com/paper/2105.14766/) (2021)
- [Quaternion Sparse Decomposition for Multi-focus Color Image Fusion](https://mlsanity.com/paper/2505.02365/) (2025)
- [Neurodynamics-Driven Coupled Neural P Systems for Multi-Focus Image Fusion](https://mlsanity.com/paper/2509.17704/) (2025)
- [ReDiffuse: Rotation Equivariant Diffusion Model for Multi-focus Image Fusion](https://mlsanity.com/paper/2603.21129/) (2026)

## Abstract (arXiv)

Multi-focus image fusion (MFF) is a popular technique to generate an all-in-focus image, where all objects in the scene are sharp. However, existing methods pay little attention to defocus spread effects of the real-world multi-focus images. Consequently, most of the methods perform badly in the areas near focus map boundaries. According to the idea that each local region in the fused image should be similar to the sharpest one among source images, this paper presents an optimization-based approach to reduce defocus spread effects. Firstly, a new MFF assessmentmetric is presented by combining the principle of structure similarity and detected focus maps. Then, MFF problem is cast into maximizing this metric. The optimization is solved by gradient ascent. Experiments conducted on the real-world dataset verify superiority of the proposed model. The codes are available at https://github.com/xsxjtu/MFF-SSIM.