  This paper tackles a new problem setting: reinforcement learning with
pixel-wise rewards (pixelRL) for image processing. After the introduction of
the deep Q-network, deep RL has been achieving great success. However, the
applications of deep reinforcement learning (RL) for image processing are still
limited. Therefore, we extend deep RL to pixelRL for various image processing
applications. In pixelRL, each pixel has an agent, and the agent changes the
pixel value by taking an action. We also propose an effective learning method
for pixelRL that significantly improves the performance by considering not only
the future states of the own pixel but also those of the neighbor pixels. The
proposed method can be applied to some image processing tasks that require
pixel-wise manipulations, where deep RL has never been applied. Besides, it is
possible to visualize what kind of operation is employed for each pixel at each
iteration, which would help us understand why and how such an operation is
chosen. We also believe that our technology can enhance the explainability and
interpretability of the deep neural networks. In addition, because the
operations executed at each pixels are visualized, we can change or modify the
operations if necessary. We apply the proposed method to a variety of image
processing tasks: image denoising, image restoration, local color enhancement,
and saliency-driven image editing. Our experimental results demonstrate that
the proposed method achieves comparable or better performance, compared with
the state-of-the-art methods based on supervised learning. The source code is
available on https://github.com/rfuruta/pixelRL.
