  Different from traditional image super-resolution task, real image
super-resolution(Real-SR) focus on the relationship between real-world
high-resolution(HR) and low-resolution(LR) image. Most of the traditional image
SR obtains the LR sample by applying a fixed down-sampling operator. Real-SR
obtains the LR and HR image pair by incorporating different quality optical
sensors. Generally, Real-SR has more challenges as well as broader application
scenarios. Previous image SR methods fail to exhibit similar performance on
Real-SR as the image data is not aligned inherently. In this article, we
propose a Dual-path Dynamic Enhancement Network(DDet) for Real-SR, which
addresses the cross-camera image mapping by realizing a dual-way dynamic
sub-pixel weighted aggregation and refinement. Unlike conventional methods
which stack up massive convolutional blocks for feature representation, we
introduce a content-aware framework to study non-inherently aligned image pair
in image SR issue. First, we use a content-adaptive component to exhibit the
Multi-scale Dynamic Attention(MDA). Second, we incorporate a long-term skip
connection with a Coupled Detail Manipulation(CDM) to perform collaborative
compensation and manipulation. The above dual-path model is joint into a
unified model and works collaboratively. Extensive experiments on the
challenging benchmarks demonstrate the superiority of our model.
