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, which limits their practical usages...
believe that some corrupted image regions are inherently easier to restore than
others since the distortion and content vary within an image. To this end, we
propose Path-Restore, a multi-path CNN with a pathfinder that could dynamically
select an appropriate route for each image region. We train the pathfinder
using reinforcement learning with a difficulty-regulated reward, which is
related to the performance, complexity and "the difficulty of restoring a
region". We conduct experiments on denoising and mixed restoration tasks. The
results show that our method could achieve comparable or superior performance
to existing approaches with less computational cost. In particular, our method
is effective for real-world denoising, where the noise distribution varies
across different regions of a single image. We surpass the state-of-the-art
CBDNet by 0.94 dB and run 29% faster on the realistic Darmstadt Noise Dataset. Models and codes will be released.