# An α-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion

arXiv: https://arxiv.org/abs/1910.13136 · HTML: https://mlsanity.com/paper/1910.13136/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 7 of 7)

- [Image Fusion via Vision-Language Model](https://mlsanity.com/paper/2402.02235/) (2024)
- [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)
- [Towards Real-World Focus Stacking with Deep Learning](https://mlsanity.com/paper/2311.17846/) (2023)
- [Neurodynamics-Driven Coupled Neural P Systems for Multi-Focus Image Fusion](https://mlsanity.com/paper/2509.17704/) (2025)
- [Inter-Image Pixel Shuffling for Multi-focus Image Fusion](https://mlsanity.com/paper/2603.07120/) (2026)
- [Clarity Contrast and Similarity Selection for Multi-Focus Image Fusion](https://mlsanity.com/paper/2608.03252/) (2026)

## Abstract (arXiv)

Capturing an all-in-focus image with a single camera is difficult since the depth of field of the camera is usually limited. An alternative method to obtain the all-in-focus image is to fuse several images focusing at different depths. However, existing multi-focus image fusion methods cannot obtain clear results for areas near the focused/defocused boundary (FDB). In this paper, a novel α-matte boundary defocus model is proposed to generate realistic training data with the defocus spread effect precisely modeled, especially for areas near the FDB. Based on this α-matte defocus model and the generated data, a cascaded boundary aware convolutional network termed MMF-Net is proposed and trained, aiming to achieve clearer fusion results around the FDB. More specifically, the MMF-Net consists of two cascaded sub-nets for initial fusion and boundary fusion, respectively; these two sub-nets are designed to first obtain a guidance map of FDB and then refine the fusion near the FDB. Experiments demonstrate that with the help of the new α-matte boundary defocus model, the proposed MMF-Net outperforms the state-of-the-art methods both qualitatively and quantitatively.