# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 1 Introduction  
Multi-focus image fusion (MFF) is a crucial technique in optical imaging that addresses the limited depth of field by integrating multiple source images, each focused at a different depth, into a single all-in-focus representation. By extracting and combining the optimally focused regions, MFF algorithms produce a visually coherent and comprehensive fused image. This approach has broad applications, including microscopy [1], textile quality inspection [2], and autonomous driving [3]. Traditional MFF methods are categorized into two main groups: transform domain-based and spatial domain-based approaches. Transform domain methods, such as multi-scale decomposition and sparse representation, project images into alternative domains for processing [4, 5, 6, 7, 8, 9]. While effective, these methods often suffer from intensity inconsistencies [10] and noise sensitivity [11]. Spatial domain methods directly process pixels usingtechniques like block-based [12; 13], region-based [14; 15], and pixel-level strategies [16; 17]. However, these methods typically rely on handcrafted features that lack expressiveness, especially in homogeneous regions [18], and can introduce artifacts along object boundaries [19]. More recent advancements, such as MDLSR_RFM [20], have improved traditional methods by using multi-dictionary linear sparse representation for focus detection and edge-weighting matting for boundary refinement. PADCDTNP [21] introduced a self-adjusting, dual-channel neural system for more accurate focused region identification. Despite these improvements, the increasing complexity of traditional methods leads to greater computational demands. The advent of deep learning has revolutionized MFF. By leveraging the parallel processing capabilities of modern GPUs, deep learning-based methods have surpassed many traditional approaches in both fusion quality and processing efficiency. These methods learn from large datasets, automating feature extraction and fusion and eliminating the need for complex, handcrafted schemes. Early deep learning methods framed MFF as a classification task. They used convolutional neural networks (CNNs) to generate a decision map for subsequent fusion [10; 19; 22; 23]. By guiding network optimization with annotated decision maps, these approaches significantly reduced task complexity and improved fusion results within the same computational budget. This fusion paradigm remains a popular research topic due to its interpretability, staged optimization capabilities, and synergy with traditional image processing techniques. More recently, a representative and innovative study introduced ZMFF [11], an unsupervised and untrained deep learning model for MFF that uses a deep image prior and a deep mask prior to effectively fuse images without requiring training data. However, this method requires multiple optimization iterations, leading to very low processing efficiency and limited practical utility. Subsequently, the focus has shifted to developing general end-to-end MFF frameworks [24; 25; 26; 27; 28]. These methods aim to perform the entire fusion task--from feature extraction to reconstruction--within a single network, which eliminates the need for decision map post-processing and better handles complex scenes. However, to enhance fusion quality, some of these newer methods have sacrificed computational efficiency. For example, SwinMFF [26] achieves high pixel-value fidelity but has high computational requirements due to its self-attention mechanism. The multi-step sampling of diffusion model-based methods like FusionDiff [29] places significant strain on the hardware. The resulting high computational demands, energy consumption, and hardware costs severely limit the practical deployment of these models in resource-constrained environments, such as autonomous driving systems and edge devices.