  Deep learning for image super-resolution (SR) has been investigated by
numerous researchers in recent years. Most of the works concentrate on
effective block designs and improve the network representation but lack
interpretation. There are also iterative optimization-inspired networks for
image SR, which take the solution step as a whole without giving an explicit
optimization step. This paper proposes an unfolding iterative shrinkage
thresholding algorithm (ISTA) inspired network for interpretable image SR.
Specifically, we analyze the problem of image SR and propose a solution based
on the ISTA method. Inspired by the mathematical analysis, the ISTA block is
developed to conduct the optimization in an end-to-end manner. To make the
exploration more effective, a multi-scale exploitation block and multi-scale
attention mechanism are devised to build the ISTA block. Experimental results
show the proposed ISTA-inspired restoration network (ISTAR) achieves
competitive or better performances than other optimization-inspired works with
fewer parameters and lower computation complexity.
