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  • 1Centre for Intelligent Cloud Computing, COE for Advanced Cloud, Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, Malaka, 75450, Malaysia.

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Este estudio presenta el modelo ERUI-MSAF (Efficient Restoration of Underwater Images Using Multi-Scale Attention Features). El modelo ERUI-MSAF mejora eficazmente la visibilidad y la calidad de las imágenes submarinas, superando a los métodos existentes.

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filtrado bilateral adaptativoaprendizaje profundocaracterísticas de atención multiescalarestauraciónimágenes submarinas

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Área de la Ciencia:

  • Visión por Computadora
  • Procesamiento de Imágenes
  • Aprendizaje Profundo

Sus antecedentes:

  • Las imágenes submarinas sufren degradaciones como desenfoque, bajo contraste y desviación de color.
  • La restauración de imágenes submarinas es crucial para diversas aplicaciones prácticas.
  • Los métodos tradicionales tienen dificultades con las degradaciones complejas de las imágenes submarinas.

Objetivo del estudio:

  • Desarrollar un método eficaz para la restauración de imágenes submarinas.
  • Mejorar la visibilidad y la calidad general de las imágenes submarinas.
  • Presentar el modelo ERUI-MSAF (Efficient Restoration of Underwater Images Using Multi-Scale Attention Features).

Principales métodos:

  • Filtrado bilateral adaptativo (ABF) para reducción de ruido y preprocesamiento.
  • Modelo ERUI-MSAF que integra características de atención espacial y de canal.
  • Fusión de Deep Wavenet (DWN) para características espaciales y EfficientNet para características de canal.

Principales resultados:

  • El modelo ERUI-MSAF enfatiza adaptativamente las características y regiones informativas.
  • Se obtuvieron valores superiores de relación señal-ruido (PSNR) de 34.258 y 29.0073 en los conjuntos de datos EUVP y UIEB.
  • Se demostró un alto rendimiento y eficiencia computacional en comparación con los modelos existentes.

Conclusiones:

  • El modelo ERUI-MSAF propuesto es eficaz para la restauración de imágenes submarinas.
  • La integración de características de atención multiescala mejora significativamente la calidad de la imagen.
  • El método ofrece una solución prometedora para mejorar las imágenes submarinas.