Reconstruction-based super-resolution algorithms require very accurate alignment and good choice of filters to be effective. Often these requirements are hard to satisfy, for example, when we adopt optical flow as the motion model. In addition, the condition of having enough sub-samples may vary from pixel to pixel. In this paper, we propose an alternative super-resolution method based on image fusion (called super-fusion hereafter). Image fusion has been proven to be effective in many applications. Extending image fusion to super-resolve images, we show that super-fusion is a faster alternative that imposes less requirements and is more stable than traditional super-resolution methods.