File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/ICIP.2014.7025816
- Scopus: eid_2-s2.0-84949926534
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Image restoration via Bayesian structured sparse coding
Title | Image restoration via Bayesian structured sparse coding |
---|---|
Authors | |
Keywords | alternative minimization Bayesian sparse coding Gaussian scale mixture structured sparsity variational image restoration |
Issue Date | 2014 |
Citation | 2014 IEEE International Conference on Image Processing, ICIP 2014, 2014, p. 4018-4022 How to Cite? |
Abstract | In this work, we propose a Bayesian structured sparse coding (BSSC) framework containing a nonlocal extension of Gaussian scale mixture (GSM) model by exploiting structured sparsity. It is shown that the variances of sparse coefficients (the field of Gaussian scalars) - if treated as a latent variable - can be jointly estimated along with the unknown sparse coefficients via the the method of alternative optimization. When applied to image restoration, BSSC leads to closed-form solutions involving iterative shrinkage/filtering and therefore admits computationally efficient implementation. Our experimental results have shown that BSSC-based image restoration often delivers reconstructed images with higher subjective/objective qualities than other competing approaches including IDD-BM3D and NCSR. |
Persistent Identifier | http://hdl.handle.net/10722/327073 |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dong, Weisheng | - |
dc.contributor.author | Li, Xin | - |
dc.contributor.author | Ma, Yi | - |
dc.contributor.author | Shi, Guangming | - |
dc.date.accessioned | 2023-03-31T05:28:36Z | - |
dc.date.available | 2023-03-31T05:28:36Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | 2014 IEEE International Conference on Image Processing, ICIP 2014, 2014, p. 4018-4022 | - |
dc.identifier.uri | http://hdl.handle.net/10722/327073 | - |
dc.description.abstract | In this work, we propose a Bayesian structured sparse coding (BSSC) framework containing a nonlocal extension of Gaussian scale mixture (GSM) model by exploiting structured sparsity. It is shown that the variances of sparse coefficients (the field of Gaussian scalars) - if treated as a latent variable - can be jointly estimated along with the unknown sparse coefficients via the the method of alternative optimization. When applied to image restoration, BSSC leads to closed-form solutions involving iterative shrinkage/filtering and therefore admits computationally efficient implementation. Our experimental results have shown that BSSC-based image restoration often delivers reconstructed images with higher subjective/objective qualities than other competing approaches including IDD-BM3D and NCSR. | - |
dc.language | eng | - |
dc.relation.ispartof | 2014 IEEE International Conference on Image Processing, ICIP 2014 | - |
dc.subject | alternative minimization | - |
dc.subject | Bayesian sparse coding | - |
dc.subject | Gaussian scale mixture | - |
dc.subject | structured sparsity | - |
dc.subject | variational image restoration | - |
dc.title | Image restoration via Bayesian structured sparse coding | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ICIP.2014.7025816 | - |
dc.identifier.scopus | eid_2-s2.0-84949926534 | - |
dc.identifier.spage | 4018 | - |
dc.identifier.epage | 4022 | - |