Image fusion, especially in the context of multi-focus image fusion, plays a crucial role in digital image processing by enhancing the clarity and detail of visual content through the combination of multiple source images. Traditional spatial domain methods often suffer from issues like spectral distortion and low contrast, which has led researchers to explore techniques in the frequency domain, such as the Discrete Cosine Transform (DCT). DCT-based methods are particularly valued for their computational efficiency, making them a strong alternative, especially in applications like image compression and fusion. This study focuses on DCT-based approaches, including variants that incorporate Singular Value Decomposition (SVD) and a combination of Correlation Coefficient with Energy-Correlation (Corr_Eng), both with and without Consistency Verification (CV). Extensive testing on multi-focus image datasets revealed that the DCT + SVD + CV method consistently shows better results in both qualitative and quantitative assessments. This indicates that integrating DCT+SVD+CV provides a powerful approach for achieving effective and efficient image fusion.