Visibility in underwater robotics: Benchmarking and single image dehazing
Dealing with underwater visibility is one of the most important challenges in autonomous underwater robotics. The light transmission in the water medium degrades images making the interpretation of the scene difficult and consequently compromising the whole intervention. This thesis contributes by a...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/432778 |
| Acceso en línea: | http://hdl.handle.net/10803/432778 http://dx.doi.org/10.6035/14028.2017.178642 http://mediaserver.csuc.cat/tdx/documents/48/65/15/48651557423080424863754938277457379135/ |
| Access Level: | acceso abierto |
| Palabra clave: | Robótica submarina Benchmarking Dehazing Aprendizaje automático Simulación de robots Visibilidad submarina Tecnologies de la Informació i les Comunicacions (TIC) 68 |
| Sumario: | Dealing with underwater visibility is one of the most important challenges in autonomous underwater robotics. The light transmission in the water medium degrades images making the interpretation of the scene difficult and consequently compromising the whole intervention. This thesis contributes by analysing the impact of the underwater image degradation in commonly used vision algorithms through benchmarking. An online framework for underwater research that makes possible to analyse results under different conditions is presented. Finally, motivated by the results of experimentation with the developed framework, a deep learning solution is proposed capable of dehazing a degraded image in real time restoring the original colors of the image. |
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