Research on signal and image denoising techniques
(English) This dissertation aims to investigate two crucial tasks in the field of signal and image processing: signal denoising and image enhancement. Firstly, for signal processing, we propose three innovative denoising algorithms tailored specifically for one-dimensional EEG signals. These algorit...
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| Tipo de recurso: | tesis doctoral |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/424238 |
| Acceso en línea: | https://hdl.handle.net/2117/424238 https://dx.doi.org/10.5821/dissertation-2117-424238 |
| Access Level: | acceso abierto |
| Palabra clave: | 621.3 Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| Sumario: | (English) This dissertation aims to investigate two crucial tasks in the field of signal and image processing: signal denoising and image enhancement. Firstly, for signal processing, we propose three innovative denoising algorithms tailored specifically for one-dimensional EEG signals. These algorithms combine the strengths of deep learning and traditional signal processing techniques to effectively adapt to various noise types associated with different cognitive tasks, thereby enhancing the quality and accuracy of the signals. Secondly, in the domain of image processing, we introduce three novel image enhancement algorithms designed to tackle multiple noise types in natural scenes. By integrating deep learning methodologies with prior knowledge, these algorithms enhance image sharpness, contrast, and detail reproduction, demonstrating adaptability and reliability across different lighting, weather conditions, and photographic equipment. Lastly, we conduct a comprehensive analysis of the similarities and differences between image enhancement and signal denoising tasks. Through comparing the methodologies employed by each in handling diverse noise types, we derive meaningful conclusions to guide future research. These contributions are poised to significantly advance technological capabilities and theoretical understanding in both domains. |
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