A Teacher-Learning-Based Optimization Approach for Blur Detection in Defocused Images

Defocus blur is often encountered in images taken with optical imaging equipment. It might be unwanted, but it might also be a deliberate artistic effect, which means it might help how we see the scenario in an image. In specific applications like image restoration or object detection, there may be a need to divide a partially blurred image into its blurred and sharp regions. The effectiveness of blur detection is influenced by how features are combined. In this paper, we propose a parameter-free metaheuristic optimization strategy known as teacher-learning-based optimization (TLBO) to find an optimal weight vector for the combination of blur maps. First, we compute multi-scale blur maps, i.e., features using an LBP-based blur metric. Then, we apply a regularization scheme to refine the initial blur maps. This results in a smooth, edge-preserving blur map that leverages structural information for improved segmentation. Lastly, TLBO is used to find the optimal weight vectors of each refined blur map for the linear feature combination. The proposed model is validated through extensive experiments on two benchmark datasets, and its performance is comparable against five state-of-the-art methods.