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Thông tin trách nhiệm: Daumas-Ladouce, Federico; Garcı'a-Torres, Miguel; Noguera, Jos´e Luis V´azquez; Pinto-Roa, Diego P.; Legal-Ayala, Horacio
Nhà Xuất Bản: Elsevier
Năm Xuất Bản: 2020
Several histogram equalization methods focus on enhancing the contrast as one of their main objectives, but usually without considering the details of the input image. Other methods seek to keep the brightness while improving the contrast, causing distortion. Among the multi-objective algorithms, the classical optimization (a priori) techniques are commonly used given their simplicity. One of the most representative method is the weighted sum of metrics used to enhance the contrast of an image. These type of techniques, beside just returning a single image, have problems related to the weight assignment for each selected metric. To avoid the pitfalls of the algorithms just mentioned, we propose a new method called MOPHE (Multi-Objective Pareto Histogram Equalization) which is based on Multi-objective Particle Swarm Optimization (MOPSO) approach combining different metrics in a posteriori selection criteria context.
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