  Image restoration is the task of recovering a clean image from a degraded
version. In most cases, the degradation is spatially varying, and it requires
the restoration network to both localize and restore the affected regions. In
this paper, we present a new approach suitable for handling the image-specific
and spatially-varying nature of degradation in images affected by practically
occurring artifacts such as blur, rain-streaks. We decompose the restoration
task into two stages of degradation localization and degraded region-guided
restoration, unlike existing methods which directly learn a mapping between the
degraded and clean images. Our premise is to use the auxiliary task of
degradation mask prediction to guide the restoration process. We demonstrate
that the model trained for this auxiliary task contains vital region knowledge,
which can be exploited to guide the restoration network's training using
attentive knowledge distillation technique. Further, we propose mask-guided
convolution and global context aggregation module that focuses solely on
restoring the degraded regions. The proposed approach's effectiveness is
demonstrated by achieving significant improvement over strong baselines.

Image and Video Processing;Computer Vision and Pattern Recognition