  Deep convolutional neural networks (DCNNs) have recently demonstrated
high-quality results in single-image super-resolution (SR). DCNNs often suffer
from over-parametrization and large amounts of redundancy, which results in
inefficient inference and high memory usage, preventing massive applications on
mobile devices. As a way to significantly reduce model size and computation
time, binarized neural network has only been shown to excel on semantic-level
tasks such as image classification and recognition. However, little effort of
network quantization has been spent on image enhancement tasks like SR, as
network quantization is usually assumed to sacrifice pixel-level accuracy. In
this work, we explore an network-binarization approach for SR tasks without
sacrificing much reconstruction accuracy. To achieve this, we binarize the
convolutional filters in only residual blocks, and adopt a learnable weight for
each binary filter. We evaluate this idea on several state-of-the-art
DCNN-based architectures, and show that binarized SR networks achieve
comparable qualitative and quantitative results as their real-weight
counterparts. Moreover, the proposed binarized strategy could help reduce model
size by 80% when applying on SRResNet, and could potentially speed up inference
by 5 times.
