  Advances in image super-resolution (SR) have recently benefited significantly
from rapid developments in deep neural networks. Inspired by these recent
discoveries, we note that many state-of-the-art deep SR architectures can be
reformulated as a single-state recurrent neural network (RNN) with finite
unfoldings. In this paper, we explore new structures for SR based on this
compact RNN view, leading us to a dual-state design, the Dual-State Recurrent
Network (DSRN). Compared to its single state counterparts that operate at a
fixed spatial resolution, DSRN exploits both low-resolution (LR) and
high-resolution (HR) signals jointly. Recurrent signals are exchanged between
these states in both directions (both LR to HR and HR to LR) via delayed
feedback. Extensive quantitative and qualitative evaluations on benchmark
datasets and on a recent challenge demonstrate that the proposed DSRN performs
favorably against state-of-the-art algorithms in terms of both memory
consumption and predictive accuracy.
