Learning Image Filtering from a Gold Sample Based on Genetic Optimization of Morphological Processing

This paper deals with the design of a semi-automated noise filtering approach, which receives just original noisy image and corresponding gold (user manipulated) image to learn filtering task. It tries to generate an optimized mathematical morphology procedure for image filtering by applying a genetic algorithm as an optimizer. After training and generating a morphological procedure, the approach is ready to apply the learned procedure on new noisy images. The main advantage of this approach is that it takes just one gold sample to learn filtering and does not need any prior context knowledge. Using the morphological operators makes the filtering procedure robust, effective, and computationally efficient. Furthermore, the proposed filter shows little distortion on the noise free parts of an image and it can extract objects from heavily noisy environments. Architecture of the system and details of implementation are presented. The approach feasibility is tested by well-prepared synthetic noisy images and results are given and discussed.