Multi-Focus Image Fusion (MFIF) plays an important role in the field of computer vision. It aims to merge multiple images that possess different focus depths, resulting in a single image with a focused appearance. Though deep learning based methods have demonstrated development in the MFIF field, they vary significantly with regard to fusion quality and robustness to different focus changes. This paper presents the performance analysis of three deep learning-based MFIF methods specifically ECNN (Ensemble based Convolutional Neural Network), DRPL (Deep Regression Pair Learning) and SESF-Fuse. These techniques have been selected due to their publicly availability of training and testing source code, facilitating a thorough and reproducible analysis along with their diverse architectural approaches to MFIF. For training, three datasets were used ILSVRC2012, COCO2017, and DIV2K. The performance of the techniques was evaluated on two publicly available MFIF datasets: Lytro and RealMFF datasets using four objective evaluation metrics viz. Mutual Information, Gradient based metric, Piella metric and Chen-Varshney metric. Extensive experiments were conducted both qualitatively and quantitatively to analyze the effectiveness of each technique in terms of preserving details, artifacts reduction, consistency at the boundary region, texture fidelity etc. which jointly determine the feasibility of these methods for real-world applications. Ultimately, the findings illuminate the strengths and limitations of these deep learning approaches, providing valuable insights for future research and development in methodologies for MFIF.