# Multi-focus Image Fusion using dictionary learning and Low-Rank Representation

arXiv: https://arxiv.org/abs/1804.08355 · HTML: https://mlsanity.com/paper/1804.08355/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 17 of 17)

- [RFN-Nest: An end-to-end residual fusion network for infrared and visible images](https://mlsanity.com/paper/2103.04286/) (2021)
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- [Collaborative Representation for SPD Matrices with Application to Image-Set Classification](https://mlsanity.com/paper/2201.08962/) (2022)
- [Infrared and visible image fusion based on Multi-State Contextual Hidden Markov Model](https://mlsanity.com/paper/2201.10739/) (2022)
- [D2-LRR: A Dual-Decomposed MDLatLRR Approach for Medical Image Fusion](https://mlsanity.com/paper/2206.15179/) (2022)
- [FusionRegister: Every Infrared and Visible Image Fusion Deserves Registration](https://mlsanity.com/paper/2603.07667/) (2026)
- [Face recognition via compact second order image gradient orientations](https://mlsanity.com/paper/2201.09246/) (2022)
- [Deep Decomposition Network for Image Processing: A Case Study for Visible and Infrared Image Fusion](https://mlsanity.com/paper/2102.10526/) (2021)
- [Res2NetFuse: A Novel Res2Net-based Fusion Method for Infrared and Visible Images](https://mlsanity.com/paper/2112.14540/) (2021)
- [Unsupervised Image Fusion Method based on Feature Mutual Mapping](https://mlsanity.com/paper/2201.10152/) (2022)
- [Low-rank features based double transformation matrices learning for image classification](https://mlsanity.com/paper/2201.12351/) (2022)
- [UHD-MFF: Shattering Barriers in Multi-Focus Ultra-High-Definition Image Fusion via Learnable Lookup Tables](https://mlsanity.com/paper/2606.31242/) (2026)

## Abstract (arXiv)

Among the representation learning, the low-rank representation (LRR) is one of the hot research topics in many fields, especially in image processing and pattern recognition. Although LRR can capture the global structure, the ability of local structure preservation is limited because LRR lacks dictionary learning. In this paper, we propose a novel multi-focus image fusion method based on dictionary learning and LRR to get a better performance in both global and local structure. Firstly, the source images are divided into several patches by sliding window technique. Then, the patches are classified according to the Histogram of Oriented Gradient (HOG) features. And the sub-dictionaries of each class are learned by K-singular value decomposition (K-SVD) algorithm. Secondly, a global dictionary is constructed by combining these sub-dictionaries. Then, we use the global dictionary in LRR to obtain the LRR coefficients vector for each patch. Finally, the l_1-norm and choose-max fuse strategy for each coefficients vector is adopted to reconstruct fused image from the fused LRR coefficients and the global dictionary. Experimental results demonstrate that the proposed method can obtain state-of-the-art performance in both qualitative and quantitative evaluations compared with serval classical methods and novel methods.The Code of our fusion method is available at https://github.com/hli1221/imagefusion_dllrr