PixelRL: Fully Convolutional Network With Reinforcement Learning for Image Processing

• Published in: IEEE transactions on multimedia Vol. 22; no. 7; pp. 1704 - 1719
• Main Authors: Furuta, Ryosuke, Inoue, Naoto, Yamasaki, Toshihiko
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Convolution; denoising; Image color analysis; Image denoising; Image enhancement; Image processing; Image restoration; Learning systems; local color enhancement; Machine learning; Noise reduction; Performance enhancement; Pixels; Reinforcement learning (RL); restoration; saliency-driven image editing; Source code; Task analysis
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2019.2960636

This article 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 .