End-to-end Alternating Optimization for Real-World Blind Super
  Resolution
Zhengxiong Luo Yan Huang Shang Li Liang Wang Tieniu Tan
Blind Super-Resolution (SR) usually involves two sub-problems: 1) estimating
the degradation of the given low-resolution (LR) image; 2) super-resolving the
LR image to its high-resolution (HR) counterpart. Both problems are ill-posed
due to the information loss in the degrading process. Most previous methods try
to solve the two problems independently, but often fall into a dilemma: a good
super-resolved HR result requires an accurate degradation estimation, which
however, is difficult to be obtained without the help of original HR
information. To address this issue, instead of considering these two problems
independently, we adopt an alternating optimization algorithm, which can
estimate the degradation and restore the SR image in a single model.
Specifically, we design two convolutional neural modules, namely
\textit{Restorer} and \textit{Estimator}. \textit{Restorer} restores the SR
image based on the estimated degradation, and \textit{Estimator} estimates the
degradation with the help of the restored SR image. We alternate these two
modules repeatedly and unfold this process to form an end-to-end trainable
network. In this way, both \textit{Restorer} and \textit{Estimator} could get
benefited from the intermediate results of each other, and make each
sub-problem easier. Moreover, \textit{Restorer} and \textit{Estimator} are
optimized in an end-to-end manner, thus they could get more tolerant of the
estimation deviations of each other and cooperate better to achieve more robust
and accurate final results. Extensive experiments on both synthetic datasets
and real-world images show that the proposed method can largely outperform
state-of-the-art methods and produce more visually favorable results. The codes
are rleased at \url{https://github.com/greatlog/RealDAN.git}.