Efficient Parameter Estimation in Image Processing using a Multi-Agent Hysteretic Q-Learning Approach

Abstract

Optimizing image processing parameters is often a time-consuming and unreliable task that requires manual adjustments. In this paper, we present a novel approach that utilizes a multi-agent system with Hysteretic Q-learning to automatically optimize these parameters, providing a more efficient solution. We conducted an empirical study that focused on extracting objects of interest from textural images to validate our approach. Experimental results demonstrate that our multiagent approach outperforms the traditional single-agent approach by quickly finding optimal parameter values and producing satisfactory results. Our approach's key innovation is the ability to enable agents to cooperate and optimize their behavior for the given task through the use of a multi-agent system. This feature distinguishes our approach from previous work that only used a single agent. By incorporating reinforcement learning techniques in a multi-agent context, our approach provides a scalable and effective solution to parameter optimization in image processing.