A variety of super-resolution algorithms have been described in this book. Most of them are based on the same source of information however; that the super-resolution image should generate the lower resolution input images when appropriately warped and down-sampled to model image formation. (This information is usually incorporated into super-resolution algorithms in the form of reconstruction constraints which are frequently combined with a smoothness prior to regularize their solution.) In this final chapter, we first investigate how much extra information is actually added by having more than one image for super-resolution. In particular, we derive a sequence of analytical results which show that the reconstruction constraints provide far less useful information as the decimation ratio increases. We validate these results empirically and show that for large enough decimation ratios any smoothness prior leads to overly smooth results with very little high-frequency content however many (noiseless) low resolution input images are used. In the second half of this chapter, we propose a super-resolution algorithm which uses a completely different source of information, in addition to the reconstruction constraints. The algorithm recognizes local “features” in the low resolution images and then enhances their resolution in an appropriate manner, based on a collection of high and low-resolution training samples. We call such an algorithm a hallucination algorithm.