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Very deep Convolutional Neural Networks (CNNs) have greatly improved theperformance on various image restoration tasks. However, this comes at a priceof increasing computational burden, hence limiting their practical usages. Weobserve that some corrupted image regions are inherently easier to restore thanothers since the distortion and content vary within an image. To leverage this,we propose Path-Restore, a multi-path CNN with a pathfinder that candynamically select an appropriate route for each image region. We train thepathfinder using reinforcement learning with a difficulty-regulated reward.This reward is related to the performance, complexity and "the difficulty ofrestoring a region". A policy mask is further investigated to jointly processall the image regions. We conduct experiments on denoising and mixedrestoration tasks. The results show that our method achieves comparable orsuperior performance to existing approaches with less computational cost. Inparticular, Path-Restore is effective for real-world denoising, where the noisedistribution varies across different regions on a single image. Compared to thestate-of-the-art RIDNet, our method achieves comparable performance and runs2.7x faster on the realistic Darmstadt Noise Dataset.

Path-Restore: Learning Network Path Selection for Image Restoration