Image Denoising by Convolutional Neural Network
碩士 === 國立清華大學 === 資訊系統與應用研究所 === 107 === Removing noise from the images to improve image quality is the main challenge in image processing. Especially as the ubiquitous spread of computers, smartphones, the Internet, and social networks, image denoising becomes more and more important. In this work...
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| Format: | Others |
| Language: | en_US |
| Published: |
2019
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| Online Access: | http://ndltd.ncl.edu.tw/handle/he56yv |
| Summary: | 碩士 === 國立清華大學 === 資訊系統與應用研究所 === 107 === Removing noise from the images to improve image quality is the main challenge in image processing. Especially as the ubiquitous spread of computers, smartphones, the Internet, and social networks, image denoising becomes more and more important.
In this work, we extend upon the results of Ulyanov et al.~\cite{Ulyanov_2018_CVPR} and introduce a competitive image denoising method based on the structure characteristic of convolutional neural networks (CNNs). Different from most CNN-based methods which need a large-scale dataset for training, our method only looks at one degraded image and removes noise on itself. This method is not only an application of image denoising but also a point of view for visualizing the property and effect of each element in convolutional neural networks.
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