Fuzzy algorithms for image enhancement and edge detection

In this thesis we investigate how artificial intelligent techniques, namely fuzzy logic and genetic/evolutionary algorithms can be used for digital image processing applications. We demonstrate our techniques with respect to two main research areas: removal of heavy impulse noise from corrupted gray scale images and edge detection in digital images. Very often fuzzy logic systems need to deal with large number of rules. This results in two major design issues: (i) How to formulate the fuzzy knowledge base using human expertise and experience? (ii) How to reduce the high computational power and the high processing times required? In this thesis we use evolutionary algorithms (including coevolutionary algorithms) to learn fuzzy knowledge bases to handle the design issue (i) described above, while using multi-layered and hierarchical fuzzy logic systems to reduce the number of rules and hence the computational overhead involved, thereby addressing issue (ii) stated above. In this research, when fuzzy rules are learnt using evolutionary algorithms, each individual in the evolutionary algorithm is appropriately encoded to uniquely represent the fuzzy knowledge base. The fitness of each individual in the evolutionary algorithm is calculated with respect to a predefined reference. In the case of an algorithm learning to enhance a digital image this reference is often associated with the uncorrupted perfect image. Designing multi-layered and hierarchical fuzzy structures involves breaking down the total number of rules, to be fed into multiple fuzzy layers in the system. This process needs careful consideration in forming the appropriate fuzzy layers as well as deciding the parameters to be input to different layers, so that the desired result is obtained with highest precision using the least computation time. Coevolutionary algorithms are powerful tools that can be used in situations where several factors contributing towards the system performance need to be learnt simultaneously. Here multiple populations consisting of candidate solutions are evolved in parallel and the fitness of individuals in each of the population are evaluated by forming a vector of candidate solutions selected from each population. The artificial intelligence techniques briefly described above will be used in this thesis with application to enhancement and edge detection in digital images.