Abstract
A new approach to image enhancement based on fusion of a number 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, sharpening, and Wiener filters, can resolve this difficulty. Many different techniques already exist in literatures. In this work, a reinforcement-learning agent will be proposed During learning, the agent takes some actions (i.e., different weights for filters) to change its environment (the image quality). Reinforcement is provided by a scalar evaluation determined subjectively by the user. The approach has several advantages. The user interaction eliminates the need for objective image quality measures. No formal user model is required. Finally, no training data is necessary. The paper describes the implementation and evaluation of a global reinforced adjustment of the weights of the different filters.
Original language | English (US) |
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Pages (from-to) | 847-850 |
Number of pages | 4 |
Journal | Canadian Conference on Electrical and Computer Engineering |
Volume | 2 |
State | Published - 2003 |
Event | CCECE 2003 Canadian Conference on Electrical and Computer Engineering: Toward a Caring and Humane Technology - Montreal, Canada Duration: May 4 2003 → May 7 2003 |
Keywords
- Filter Fusion
- Image Processing
- Reinforcement Learning
ASJC Scopus subject areas
- Hardware and Architecture
- Electrical and Electronic Engineering