  Single image super resolution aims to enhance image quality with respect to
spatial content, which is a fundamental task in computer vision. In this work,
we address the task of single frame super resolution with the presence of image
degradation, e.g., blur, haze, or rain streaks. Due to the limitations of frame
capturing and formation processes, image degradation is inevitable, and the
artifacts would be exacerbated by super resolution methods. To address this
problem, we propose a dual-branch convolutional neural network to extract base
features and recovered features separately. The base features contain local and
global information of the input image. On the other hand, the recovered
features focus on the degraded regions and are used to remove the degradation.
Those features are then fused through a recursive gate module to obtain sharp
features for super resolution. By decomposing the feature extraction step into
two task-independent streams, the dual-branch model can facilitate the training
process by avoiding learning the mixed degradation all-in-one and thus enhance
the final high-resolution prediction results. We evaluate the proposed method
in three degradation scenarios. Experiments on these scenarios demonstrate that
the proposed method performs more efficiently and favorably against the
state-of-the-art approaches on benchmark datasets.
