The one-to-one correspondence between co-occurrence image patches of two different resolutions is extensively used in example-based super-resolution (SR). Due to the dimensionality gap between low resolution (LR) and high resolution (HR) spaces, however, an LR patch may correspond to a number of HR patches in practice. This ambiguity is difficult to be overcome with examples representing a deterministic mapping. In this paper, we propose a statistical method for exploiting the one-to-many correspondence between LR and HR patches, which we call soft information and decision. Soft information means an LR patch is mapped to a pixel-wise distribution of all its possible HR counterparts, rather than a single or a limited set of HR candidates. Relying on the soft information, example-based SR is then regarded as an optimization problem to best preserve the local consistency in the recovered HR image. This problem is solved with an efficient message passing algorithm with a factor graph model. The final decision on the HR pixel value is made upon the maximum a posteriori estimation and is called a soft decision. Experimental results demonstrate the superiority of the proposed method compared with the state-of-the-art methods, in terms of both the subjective and objective quality of synthesized HR images.