  Binary neural network (BNN) provides a promising solution to deploy
parameter-intensive deep single image super-resolution (SISR) models onto real
devices with limited storage and computational resources. To achieve comparable
performance with the full-precision counterpart, most existing BNNs for SISR
mainly focus on compensating the information loss incurred by binarizing
weights and activations in the network through better approximations to the
binarized convolution. In this study, we revisit the difference between BNNs
and their full-precision counterparts and argue that the key for good
generalization performance of BNNs lies on preserving a complete full-precision
information flow as well as an accurate gradient flow passing through each
binarized convolution layer. Inspired by this, we propose to introduce a
full-precision skip connection or its variant over each binarized convolution
layer across the entire network, which can increase the forward expressive
capability and the accuracy of back-propagated gradient, thus enhancing the
generalization performance. More importantly, such a scheme is applicable to
any existing BNN backbones for SISR without introducing any additional
computation cost. To testify its efficacy, we evaluate it using four different
backbones for SISR on four benchmark datasets and report obviously superior
performance over existing BNNs and even some 4-bit competitors.
