An improved teaching-learning based robust edge detection algorithm for noisy images

This paper presents an improved Teaching Learning Based Optimization (TLO) and a methodology for obtaining the edge maps of the noisy real life digital images. TLO is a population based algorithm that simulates the teaching-learning mechanism in class rooms, comprising two phases of teaching and learning. The 'Teaching Phase' represents learning from the teacher and 'Learning Phase' indicates learning by the interaction between learners. This paper introduces a third phase denoted by "Avoiding Phase" that helps to keep the learners away from the worst students with a view of exploring the problem space more effectively and escaping from the sub-optimal solutions. The improved TLO (ITLO) explores the solution space and provides the global best solution. The edge detection problem is formulated as an optimization problem and solved using the ITLO. The results of real life and medical images illustrate the performance of the developed method.