Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning

Understand

We investigate a novel approach for image restoration by reinforcement learning.

• Unlike existing studies that mostly train a single large network for a specialized task, we prepare a toolbox consisting of small-scale convolutional networks of different complexities and specialized in different tasks.
• Our method, RL-Restore, then learns a policy to select appropriate tools from the toolbox to progressively restore the quality of a corrupted image.
• We formulate a step-wise reward function proportional to how well the image is restored at each step to learn the action policy.