Multiple imaging sensors are being used in surveillance, medical imaging, and machine vision. To effectively combine the information from multiple imaging sensors, image fusion techniques have emerged to produce an improved fused single image. This paper introduces a general framework for image fusion, incorporating the structural similarity index measure (SSIM), a highly effective measure of visual proximity aligned with the human visual system. To formulate image fusion, the SSIM-Mean is used for data fidelity of an optimization problem. To solve it numerically by gradient descent method an alternate expression of SSIM is proposed, utilizing Mean values and vector norms, alongside its derivative calculation. We apply the proposed method to the resolution enhancement of a set of given degraded images, where SSIM-Mean is used as data fidelity. The results will be evaluated both qualitatively and quantitatively. A significant advantage of the proposed formulation lies in its flexibility to accommodate various degradation operators and regularization terms, which marks a notable strength in this approach. Our code is available at https://github.com/fatemehhirbodvash/SSIM-Mean- .