Using Reinforcement Learning for Filter Fusion in Image Enhancement

F. Sahba, H.R. Tizhoosh, and M.M.A. Salama (Canada)


Image Processing, Reinforcement Learning, Filter Fusion, Machine Learning.


A new approach to image enhancement based on fusion of filters using a reinforcement learning scheme is presented. In most applications the result of applying a single filter is usually unsatisfactory. Appropriate fusion of the results of several different filters, such as median, local average, and Wiener filters can resolve this difficulty. Many dif ferent techniques already exist in literatures. In this work, a reinforcement learning agent will be proposed. We use this novel method as an effective way to find appropri ate weights for various filters. During learning, the agent takes some actions (i.e., different weights for filters) to change the environment (the image quality). Using the ref erence image, the RL agent is provided by a scalar evalua tion determined objectively. The agent uses these objective rewards-punishments to explore/exploit the solution space. The values obtained using this method can be used as very valuable knowledge to initiate weights for a Q-matrix. A subjective reinforcement learning method can use such ini tial weights to start its work from an acceptable level of knowledge.

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