# Generation and Recombination for Multifocus Image Fusion with Free Number of Inputs

arXiv: https://arxiv.org/abs/2309.04657 · HTML: https://mlsanity.com/paper/2309.04657/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 61 references that are in the map.
- First posted on arXiv 9 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [SESF-Fuse: An Unsupervised Deep Model for Multi-Focus Image Fusion](https://mlsanity.com/paper/1908.01703/) (2019)
- [MFFW: A new dataset for multi-focus image fusion](https://mlsanity.com/paper/2002.04780/) (2020)
- [Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy](https://mlsanity.com/paper/2012.14678/) (2020)
- [An α-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion](https://mlsanity.com/paper/1910.13136/) (2019)
- [End-to-End Learning for Simultaneously Generating Decision Map and Multi-Focus Image Fusion Result](https://mlsanity.com/paper/2010.08751/) (2020)
- [UFA-FUSE: A novel deep supervised and hybrid model for multi-focus image fusion](https://mlsanity.com/paper/2101.04506/) (2021)

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

- [UMCFuse: A Unified Multiple Complex Scenes Infrared and Visible Image Fusion Framework](https://mlsanity.com/paper/2402.02096/) (2024)
- [FlexiD-Fuse: Flexible number of inputs multi-modal medical image fusion based on diffusion model](https://mlsanity.com/paper/2509.09456/) (2025)
- [Rethinking Few-Shot Image Fusion: Granular Ball Priors Enable General-Purpose Deep Fusion](https://mlsanity.com/paper/2504.08937/) (2025)
- [Quaternion Sparse Decomposition for Multi-focus Color Image Fusion](https://mlsanity.com/paper/2505.02365/) (2025)
- [Generative Multi-Focus Image Fusion](https://mlsanity.com/paper/2512.21495/) (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)

Multifocus image fusion is an effective way to overcome the limitation of optical lenses. Many existing methods obtain fused results by generating decision maps. However, such methods often assume that the focused areas of the two source images are complementary, making it impossible to achieve simultaneous fusion of multiple images. Additionally, the existing methods ignore the impact of hard pixels on fusion performance, limiting the visual quality improvement of fusion image. To address these issues, a combining generation and recombination model, termed as GRFusion, is proposed. In GRFusion, focus property detection of each source image can be implemented independently, enabling simultaneous fusion of multiple source images and avoiding information loss caused by alternating fusion. This makes GRFusion free from the number of inputs. To distinguish the hard pixels from the source images, we achieve the determination of hard pixels by considering the inconsistency among the detection results of focus areas in source images. Furthermore, a multi-directional gradient embedding method for generating full focus images is proposed. Subsequently, a hard-pixel-guided recombination mechanism for constructing fused result is devised, effectively integrating the complementary advantages of feature reconstruction-based method and focused pixel recombination-based method. Extensive experimental results demonstrate the effectiveness and the superiority of the proposed method.The source code will be released on https://github.com/xxx/xxx.