Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing

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 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. We apply the proposed method to three image processing tasks: image denoising, image restoration, and local color enhancement. 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.Comment: Accepted to AAAI 201

Experimental Investigation of an Adaptively Tuned Dynamic Absorber Incorporating Magnetorheological Elastomer with Self-Sensing Property

The magnetorheological elastomer (MRE) is known to belong to a class of smart materials whose elastic properties can be varied by an externally applied magnetic field. In addition to the property of the field-dependent stiffness change of the MRE, the electrical resistance of the composite is also changed by the induced strain, thereby providing a new self-sensing feature. In the present study, a novel, dynamic vibration absorber is developed using an MRE with a self-sensing function and adaptability. The natural frequency of the absorber is instantaneously tuned to a dominant frequency extracted from the strain signal of MRE. The damping performance test shows that the vibration of a system with one degree-of-freedom that is exposed to a nonstationary disturbance can be adequately reduced by the proposed frequency-tunable dynamic absorber without the use of external sensors. © 2016 Society for Experimental MechanicsIn Press / Embargo Period 12 month