File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/TIP.2019.2943214
- Scopus: eid_2-s2.0-85077496429
- WOS: WOS:000501324900005
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Deep non-local kalman network for video compression artifact reduction
Title | Deep non-local kalman network for video compression artifact reduction |
---|---|
Authors | |
Keywords | deep neural network Kalman model recursive filtering Video compression artifact reduction video restoration |
Issue Date | 2020 |
Citation | IEEE Transactions on Image Processing, 2020, v. 29, p. 1725-1737 How to Cite? |
Abstract | Video compression algorithms are widely used to reduce the huge size of video data, but they also introduce unpleasant visual artifacts due to the lossy compression. In order to improve the quality of the compressed videos, we proposed a deep non-local Kalman network for compression artifact reduction. Specifically, the video restoration is modeled as a Kalman filtering procedure and the decoded frames can be restored from the proposed deep Kalman model. Instead of using the noisy previous decoded frames as temporal information, the less noisy previous restored frame is employed in a recursive way, which provides the potential to generate high quality restored frames. In the proposed framework, several deep neural networks are utilized to estimate the corresponding states in the Kalman filter and integrated together in the deep Kalman filtering network. More importantly, we also exploit the non-local prior information by incorporating the spatial and temporal non-local networks for better restoration. Our approach takes the advantages of both the model-based methods and learning-based methods, by combining the recursive nature of the Kalman model and powerful representation ability of neural networks. Extensive experimental results on the Vimeo-90k and HEVC benchmark datasets demonstrate the effectiveness of our proposed method. |
Persistent Identifier | http://hdl.handle.net/10722/321867 |
ISSN | 2023 Impact Factor: 10.8 2023 SCImago Journal Rankings: 3.556 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lu, Guo | - |
dc.contributor.author | Zhang, Xiaoyun | - |
dc.contributor.author | Ouyang, Wanli | - |
dc.contributor.author | Xu, Dong | - |
dc.contributor.author | Chen, Li | - |
dc.contributor.author | Gao, Zhiyong | - |
dc.date.accessioned | 2022-11-03T02:21:58Z | - |
dc.date.available | 2022-11-03T02:21:58Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Image Processing, 2020, v. 29, p. 1725-1737 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321867 | - |
dc.description.abstract | Video compression algorithms are widely used to reduce the huge size of video data, but they also introduce unpleasant visual artifacts due to the lossy compression. In order to improve the quality of the compressed videos, we proposed a deep non-local Kalman network for compression artifact reduction. Specifically, the video restoration is modeled as a Kalman filtering procedure and the decoded frames can be restored from the proposed deep Kalman model. Instead of using the noisy previous decoded frames as temporal information, the less noisy previous restored frame is employed in a recursive way, which provides the potential to generate high quality restored frames. In the proposed framework, several deep neural networks are utilized to estimate the corresponding states in the Kalman filter and integrated together in the deep Kalman filtering network. More importantly, we also exploit the non-local prior information by incorporating the spatial and temporal non-local networks for better restoration. Our approach takes the advantages of both the model-based methods and learning-based methods, by combining the recursive nature of the Kalman model and powerful representation ability of neural networks. Extensive experimental results on the Vimeo-90k and HEVC benchmark datasets demonstrate the effectiveness of our proposed method. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Image Processing | - |
dc.subject | deep neural network | - |
dc.subject | Kalman model | - |
dc.subject | recursive filtering | - |
dc.subject | Video compression artifact reduction | - |
dc.subject | video restoration | - |
dc.title | Deep non-local kalman network for video compression artifact reduction | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TIP.2019.2943214 | - |
dc.identifier.scopus | eid_2-s2.0-85077496429 | - |
dc.identifier.volume | 29 | - |
dc.identifier.spage | 1725 | - |
dc.identifier.epage | 1737 | - |
dc.identifier.eissn | 1941-0042 | - |
dc.identifier.isi | WOS:000501324900005 | - |