Deep task-driven video denoising
The main contribution of this research is two folds. First, this research work explores the vast domain of video denoising, analyze challenges in designing video denoising algorithm, and study previously successful state-of-the-art video denoising techniques. Secondly, this research aims to optimize...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/138731 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The main contribution of this research is two folds. First, this research work explores the vast domain of video denoising, analyze challenges in designing video denoising algorithm, and study previously successful state-of-the-art video denoising techniques. Secondly, this research aims to optimize video denoising algorithm when the result is supplied for high-level task behind it. We propose a method that features high-level information guided video denoising that capable of achieving comparable result with the state-of-the-art denoising while preserving semantic-aware details for high-level vision tasks. |
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