Multi-Objective Pareto Histogram Equalization
| Autor: | Diego P. Pinto-Roa, Miguel García-Torres, José Luis Vázquez Noguera, Federico Daumas-Ladouce, Horacio Legal-Ayala |
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| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science Pareto principle Particle swarm optimization 020207 software engineering Context (language use) 0102 computer and information sciences 02 engineering and technology 01 natural sciences Theoretical Computer Science Set (abstract data type) 010201 computation theory & mathematics Distortion Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Algorithm Histogram equalization |
| Zdroj: | CLEI Selected Papers |
| ISSN: | 1571-0661 |
| Popis: | 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. The goal of this method is three-fold: (1) improve the contrast (2) without losing important details, (3) avoiding an excessive distortion. MOPHE, is a pure multi-objective optimization algorithm, consequently a set of trade-off optimal solutions are generated, thus providing alternative solutions to the decision-maker, allowing the selection of one or more resulting images, depending on the application needs. Experimental results indicate that MOPHE is a promising approach, as it calculates a set of trade-off optimal solutions that are better than the results obtained from representative algorithms from the state-of-the-art regarding visual quality and metrics measurement. |
| Databáze: | OpenAIRE |
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