## 1 Introduction  
In microscopic observation, ordinary optical microscopes have a very limited depth of field at high magnification, and the imaging system can only capture a sharply focused plane [1]. Multi-focus image fusion extracts the sharp regions from local focused images in video stream data, generating a single image where all regions are in focus. It offers significant advantages in increasing the depth of field in microscopic imaging and enhancing image quality [2]. Due to their high complexity and demanding requirements on computational power and resources, traditional multi-focus image fusion algorithms can only be implemented on terminals such as computers. Benefiting from their low power consumption and portability, embedded microscopic cameras have emerged as one of the most prevalent tools for image acquisition and processing [3]. However, they also face the common challenges of limited computing power and resources inherent in embedded devices. As a result, current real-time high-resolution multi-focus image fusion algorithms are not applicable to embedded microscope systems and can only be used on devices with sufficient computational power and resources [4]. Currently, research on multi-focus image fusion is mainly divided into three categories: spatial domain methods, transform domain methods, and deep learning-based methods [5]. Spatial domain methods operate directly at the pixel level for focus discrimination and fusion operations. Depending on the adopted pixel processing strategies, spatial domain fusion methods can be further classified into block-based and pixel-based approaches. [PERSON], [PERSON] et al. [6] used a gradient-weighted method to evaluate focus regions and generate an initial decision map, which was then refined using morphological opening and closing operations to correct misjudged focused and defocused areas, ultimately achieving multi-focus image fusion. [PERSON] et al. [7] proposed a block-based spatial domain multi-focus image fusion method in which the images to be fused are divided into blocks of fixed size. These blocks are then measured and aggregated based on their activity levels to accomplish the fusion. While spatial domain methods are computationally simple, they are affected by the scale of the blocks. A single block may contain both focused and defocused regions, potentially leading to block artifacts in the fusion results [8; 9; 10]. Transform domain methods first decompose images into sub-band coefficients in the transform domain using specific decomposition rules, such as multi-scale and sparse representations. Then, certain fusion strategies are applied to these sub-band coefficients to produce new coefficients, which are finally reconstructed into the fused image through inverse transformation [11; 12]. Wavelet transform, as a multi-scale decomposition tool, provides a rich set of multi-resolution and local detail information, which is beneficial for multi-focus image fusion. [PERSON] et al. [13] proposed using dual-tree complex wavelet transform, which offers shift-invariance and directional selectivity, for image fusion. Sparse representation (SR) can effectively capture image details and minimize information loss, resulting in sharper and more natural fused images, and has thus received wide attention and application. [PERSON] et al. [14] proposed an SR-based multi-focus image fusion scheme, where source images are divided into patches, and sparse coefficients are obtained using an overcomplete dictionary. These coefficients are combined based on a max-selection rule, and the fused image is reconstructed using the combined coefficients and the dictionary. However, transform domain methods suffer from poor parameter adaptability during the transformation process and are prone to boundary artifacts during inverse transformation, along with high computational complexity [15]. Deep learning models, especially convolutional neural networks (CNNs), can learn and extract high-level features from images.