Spatio-Temporal Noise Filtering using Convolutional Neural Networks with a Realistic Noise Model under Low-Light Conditions
(2019) In Master's Thesis in Mathematical Sciences FMAM05 20182Mathematics (Faculty of Engineering)
- Abstract
- Convolutional neural networks have in recent years been successfully employed for various image processing tasks, such as filtering noise. There are however relatively few published attempts for processing video in this way. Image processing methods on single images can be applied frame by frame, but often fail to consider continuity and flow between frames. In this master's thesis we constructed several fully convolutional neural network models, trained to filter noise spatially as well as temporally. We present the differences between these models and compare the performance of each of them with a noise filter from a state-of-the-art camera, as well as with a solely spatial filter. Our data was created by adding noise to clean videos... (More)
- Convolutional neural networks have in recent years been successfully employed for various image processing tasks, such as filtering noise. There are however relatively few published attempts for processing video in this way. Image processing methods on single images can be applied frame by frame, but often fail to consider continuity and flow between frames. In this master's thesis we constructed several fully convolutional neural network models, trained to filter noise spatially as well as temporally. We present the differences between these models and compare the performance of each of them with a noise filter from a state-of-the-art camera, as well as with a solely spatial filter. Our data was created by adding noise to clean videos according to a noise model which realistically simulates noise from camera sensors under low-light conditions. On a frame by frame basis, our best model outperforms the state-of-the-art camera in most situations. Despite still having minor struggles with continuity in video, clear improvement can also be seen in comparison with only spatial noise filtering. (Less)
- Popular Abstract
- Have you ever tried filming a video during nighttime? If you did, the video presumably ended up grainy and rather unattractive. We have through machine learning developed a noise filter for video, which immensely improves the quality of low-light videos. Algorithms for noise removal have existed for quite some time. However, it is only in the last few years that machine learning have been experimented with to replace these older methods. Machine learning have so far mostly been applied on single images. Our approach was to take it one step further by noise filtering videos, where more information can be utilized.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8968887
- author
- Borglund, Tim LU and Nilsson, Tor LU
- supervisor
- organization
- course
- FMAM05 20182
- year
- 2019
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Deep Learning, CNN, Image Processing, Video Denoising, Noise Filtering
- publication/series
- Master's Thesis in Mathematical Sciences
- report number
- LUTFMA-3375-2019
- ISSN
- 1404-6342
- other publication id
- 2019:E3
- language
- English
- additional info
- To contact the authors use:tim.borglund@gmail.com
- id
- 8968887
- date added to LUP
- 2019-04-12 12:46:12
- date last changed
- 2020-05-18 07:44:42
@misc{8968887, abstract = {{Convolutional neural networks have in recent years been successfully employed for various image processing tasks, such as filtering noise. There are however relatively few published attempts for processing video in this way. Image processing methods on single images can be applied frame by frame, but often fail to consider continuity and flow between frames. In this master's thesis we constructed several fully convolutional neural network models, trained to filter noise spatially as well as temporally. We present the differences between these models and compare the performance of each of them with a noise filter from a state-of-the-art camera, as well as with a solely spatial filter. Our data was created by adding noise to clean videos according to a noise model which realistically simulates noise from camera sensors under low-light conditions. On a frame by frame basis, our best model outperforms the state-of-the-art camera in most situations. Despite still having minor struggles with continuity in video, clear improvement can also be seen in comparison with only spatial noise filtering.}}, author = {{Borglund, Tim and Nilsson, Tor}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Thesis in Mathematical Sciences}}, title = {{Spatio-Temporal Noise Filtering using Convolutional Neural Networks with a Realistic Noise Model under Low-Light Conditions}}, year = {{2019}}, }