  Very deep Convolutional Neural Networks (CNNs) have greatly improved the
performance on various image restoration tasks. However, this comes at a price
of increasing computational burden, hence limiting their practical usages. We
observe that some corrupted image regions are inherently easier to restore than
others since the distortion and content vary within an image. To leverage this,
we propose Path-Restore, a multi-path CNN with a pathfinder that can
dynamically select an appropriate route for each image region. We train the
pathfinder using reinforcement learning with a difficulty-regulated reward.
This reward is related to the performance, complexity and "the difficulty of
restoring a region". A policy mask is further investigated to jointly process
all the image regions. We conduct experiments on denoising and mixed
restoration tasks. The results show that our method achieves comparable or
superior performance to existing approaches with less computational cost. In
particular, Path-Restore is effective for real-world denoising, where the noise
distribution varies across different regions on a single image. Compared to the
state-of-the-art RIDNet, our method achieves comparable performance and runs
2.7x faster on the realistic Darmstadt Noise Dataset.
