|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM260249505 |
| 003 |
DE-627 |
| 005 |
20250220034829.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231224s2016 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2016.2564643
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n0867.xml
|
| 035 |
|
|
|a (DE-627)NLM260249505
|
| 035 |
|
|
|a (NLM)27168598
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Liu, Ding
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Robust Single Image Super-Resolution via Deep Networks With Sparse Prior
|
| 264 |
|
1 |
|c 2016
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 19.05.2017
|
| 500 |
|
|
|a Date Revised 19.05.2017
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Single image super-resolution (SR) is an ill-posed problem, which tries to recover a high-resolution image from its low-resolution observation. To regularize the solution of the problem, previous methods have focused on designing good priors for natural images, such as sparse representation, or directly learning the priors from a large data set with models, such as deep neural networks. In this paper, we argue that domain expertise from the conventional sparse coding model can be combined with the key ingredients of deep learning to achieve further improved results. We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data. The network has a cascaded structure, which boosts the SR performance for both fixed and incremental scaling factors. The proposed training and testing schemes can be extended for robust handling of images with additional degradation, such as noise and blurring. A subjective assessment is conducted and analyzed in order to thoroughly evaluate various SR techniques. Our proposed model is tested on a wide range of images, and it significantly outperforms the existing state-of-the-art methods for various scaling factors both quantitatively and perceptually
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Wang, Zhaowen
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Wen, Bihan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Yang, Jianchao
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Han, Wei
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Huang, Thomas S
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 25(2016), 7 vom: 28. Juli, Seite 3194-3207
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:25
|g year:2016
|g number:7
|g day:28
|g month:07
|g pages:3194-3207
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2016.2564643
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 25
|j 2016
|e 7
|b 28
|c 07
|h 3194-3207
|