Image fusion that is a process of combining two or more images to generate new features or enhance the available information is a subfield of image processing. Image Fusion is divided into four main categories; multi-view fusion, multi-modal fusion, multi-temporal fusion, multi-focus fusion. The depth of field property of the optical lens causes the view to have different depth of field because it can focus on only one region at a time. Thus, it is difficult to obtain a completely focused image. Multi-focus image fusion aims to generate a fused image whose pixels are as well focused as possible by combining multiple images that contain the same scene but have different parts of focus. In this paper, a comparative study has been carried out between four multi-focus image fusion methods that are Unique Colors (UC-IF), Discrete Cosine Transform (DCT-IF), Discrete Wavelet Transform (DWT-IF) and Principal Component Analysis(PCA-IF). The performances of these methods are evaluated with four most commonly used metrics; Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Root Mean Square Error (RMSE) and Entropy. Although Unique Color Method is simple and easy to implement, it has been found superior to the other methods.