  Real-world image super-resolution is a practical image restoration problem
that aims to obtain high-quality images from in-the-wild input, has recently
received considerable attention with regard to its tremendous application
potentials. Although deep learning-based methods have achieved promising
restoration quality on real-world image super-resolution datasets, they ignore
the relationship between L1- and perceptual- minimization and roughly adopt
auxiliary large-scale datasets for pre-training. In this paper, we discuss the
image types within a corrupted image and the property of perceptual- and
Euclidean- based evaluation protocols. Then we propose a method, Real-World
image Super-Resolution by Exclusionary Dual-Learning (RWSR-EDL) to address the
feature diversity in perceptual- and L1- based cooperative learning. Moreover,
a noise-guidance data collection strategy is developed to address the training
time consumption in multiple datasets optimization. When an auxiliary dataset
is incorporated, RWSR-EDL achieves promising results and repulses any training
time increment by adopting the noise-guidance data collection strategy.
Extensive experiments show that RWSR-EDL achieves competitive performance over
state-of-the-art methods on four in-the-wild image super-resolution datasets.
