Electro-Optic (EO) image sensors exhibit the properties of high resolution and low noise level, but they cannot reflect information about the temperature of objects and do not work in dark environments. On the other hand, infrared (IR) image sensors exhibit the properties of low resolution and high noise level, but IR images can reflect information about the temperature of objects all the time. Therefore, in this paper, we propose a novel framework to enhance the resolution of EO images using the information (e.g., temperature) from IR images, which helps distinguish temperature variation of objects in the daytime via high-resolution EO images. The proposed novel framework involves four main steps: (1) select target objects with temperature variation in original IR images; (2) fuse original RGB color (EO) images and IR images based on image fusion algorithms; (3) blend the fused images of target objects in proportion with original gray-scale EO images; (4) superimpose the target objects' temperature information onto original EO images via the modified NTSC color space transformation. Therein, the image fusion step will be conducted by the quantitative (Yang et al. proposed adaptive multi-sensor fusion algorithm) approach in this part. Revealing temperature information in EO images for the first time is the most significant contribution of this paper. Simulation results will show the transformed EO images with the targets' temperature information.