# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 4 Experiments  
### Experimental Setup  
**Training Strategy. ** We use the DUTS dataset ([[https://saliencydetection.net/duts/](https://saliencydetection.net/duts/)]([https://saliencydetection.net/duts/](https://saliencydetection.net/duts/))) accessed on 2 June 2025 [52], which comprises 15,572 carefully curated images. This dataset is divided into 10,553 training samples and 5019 validation samples, each resized to a resolution of 256 \(\times\) 256. To generate the training pairs, we first convert the ground truth annotations into binary masks. These masks then guide the application of variable Gaussian blur to specific regions within the source images, using kernel sizes that range from 3 to 21. This process yields realistic multi-focus image pairs. The detailed procedure for generating these training data can be found in SwinMFF [26]. For network optimization, we utilized the AdamW optimizer, initialized with a learning rate of \(1\times 10^{-3}\). A CosineAnnealingLR scheduler was implemented to progressively reduce the learning rate over the course of training. The model was trained for 200 epochs with a batch size of 32. All training was performed on a high-performance computing platform equipped with dual Nvidia A6000 GPUs, supported by an Intel(R) Xeon(R) Platinum 8375C CPU operating at 2.90 GHz. The entire framework was developed in PyTorch (version 2.4.1). **Datasets for Evaluation. ** To rigorously assess LightMFF's performance, we conducted extensive experiments using three widely recognized MFF benchmarks: Lytro [46], MFFW [53], and MFI-WHU [36]. The Lytro dataset consists of 20 image pairs captured with a Lytro light-field camera and was utilized for both qualitative and quantitative evaluation. Figure 4: The adopted fusion scheme. The MFFW dataset includes 13 image pairs notable for their pronounced defocus spread effects, allowing for qualitative evaluation under challenging focus conditions. Extending our evaluation scope, the MFI-WHU dataset provides 120 synthetically generated image pairs, created using Gaussian blur, which encompass diverse scenarios, including high-contrast scenes, and was also used for qualitative assessment. **Methods for Comparison. ** To thoroughly evaluate the efficacy of the proposed LightMFF method, we performed extensive comparisons against state-of-the-art image fusion approaches across four primary categories. For transform domain methods, our comparisons included DWT [7], DTCWT [54], NSCT [8], CVT [55], GFF [56], SR [57], ASR [58], MWGF [59], ICA [60], and NSCT-SR [61]. In the spatial domain category, we evaluated SSSDI [62], QUADTREE [63], DSIFT [61], SRCF [46], GFDF [64], BRW [65], MISF [66], and MDLSR_RFM [20]. Regarding end-to-end deep learning approaches, we included IFCNN-MAX [24], U2 Fusion [25], SDNet [35], MFF-GAN [36], SwinFusion [27], MUFusion [37], FusionDiff [29], SwinMFF [26], and DDBFusion [28]. Additionally, we compared with decision map-based deep learning methods such as CNN [22], ECNN [23], DRPL [19], SESF [30], MFIF-GAN [31], MSFIN [10], GACN [67], and ZMFF [11]. For traditional methods, we either employed the default parameters as specified in their original implementations or directly used the fusion results made publicly available by their respective authors. For deep learning-based methods, we utilized the pre-trained models provided by their authors to ensure a fair comparison. This comprehensive comparison of 37 methods across four distinct categories allows for a thorough assessment of the proposed method's capabilities. It is important to note that while some related work discusses lightweight networks, we could not include them in our comparisons due to the unavailability of their codebases. Nevertheless, many highly compact networks, such as MFF-GAN [36] and SESF [30], are already featured in our evaluation. **Evaluation metrics.