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Shadow removal is an important problem in computer vision, since the presence of shadows complicates core computer vision tasks, including image segmentation and object recognition. Most state-of-the-art shadow removal methods are based on complex deep learning architectures, which require training on a large amount of data. In this paper a novel and efficient methodology is proposed aiming to provide a simple solution to shadow removal, both in terms of implementation and computational cost. The proposed methodology is fully unsupervised, based solely on color image features. Initially, the shadow region is automatically extracted by a segmentation algorithm based on Electromagnetic-Like Optimization. Superpixel-based segmentation is performed and pairs of shadowed and non-shadowed regions, which are nearest neighbors in terms of their color content, are identified as parts of the same object. The shadowed part of each pair is relighted by means of histogram matching, using the content of its non-shadowed counterpart. Quantitative and qualitative experiments on well-recognized publicly available benchmark datasets are conducted to evaluate the performance of proposed methodology in comparison to state-of-the-art methods. The results validate both its efficiency and effectiveness, making evident that solving the shadow removal problem does not necessarily require complex deep learning-based solutions.

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Βrainstorm Optimization (BSO) is a swarm-intelligence clustering-based algorithm inspired by the human brainstorming process. Electromagnetism-like Mechanism for global Optimization (EMO) is a physics-inspired optimization algorithm. In this study we propose a novel hybrid metaheuristic evolutionary algorithm that combines aspects from both BSO and EMO. The proposed algorithm, named Emotion-aware Brainstorm Optimization (EBO), is inspired by the attraction-repulsion mechanism of electromagnetism, and it is applied in a new emotion-aware brainstorming context, where positive and negative thoughts produce ideas interacting with each other. Novel contributions include a bi-polar clustering approach, a probabilistic selection operator, and a hybrid evolution process, which improves the ability of the algorithm to avoid local optima and convergence speed. A systematic comparative performance evaluation that includes sensitivity analysis, convergence velocity and dynamic fitness landscape analyses, and scalability assessment was performed using several reference benchmark functions from standard benchmark suites. The results validate the performance advantages of the proposed algorithm over relevant state-of-the-art algorithms.