Deep convolutional neural networks (CNN) have demonstrated superior performance in image super-resolution (SR) problem. However, CNNs are known to be heavily over-parameterized, and suffer from abundant redundancy. The growing size of CNNs may be incompatible with their deployment on mobile or embedded devices. Network pruning has benefited classification tasks by removing redundant parameters and associated computation. However, it has rarely been studied for SR, because existing methods assume the channel-wise features are of equal importance to the final reconstruction. On the contrary, we show the existence of uninformative feature-maps with no contribution to the task. In order to identify and remove such uninformative channels, we propose a new pruning criterion, Discriminant Information, by characterizing the dependency of the output w.r.t to the hidden-layer feature-maps. Empirically, our DI-based channel pruning algorithm is able to trim the state-of-the-art SR networks significantly (e.g. 8.7x model size compression and 3.6x CPU acceleration on SRResNet), with no quantitative or visual performance loss.