Space-Time Super-Resolution using Deep Learning-based Framework
IR@CEERI: CSIR-Central Electronics Engineering Research Institute, Pilani
View Archive Info| Field | Value | |
| Title |
Space-Time Super-Resolution using Deep Learning-based Framework
|
|
| Creator |
Sharma, M
Chaudhary, S Lall, B |
|
| Subject |
Digital Systems
|
|
| Description |
This paper introduces a novel end-to-end deep learning framework to learn the space-time super-resolution (SR) process. We propose a coupled deep convolutional auto-encoder (CDCA) which learns the non-linear mapping between convolutional features of up-sampled low- resolution (LR) video sequence patches and convolutional features of high-resolution (HR) video seQuence patches. The upsampling in LR video refers to tri-cubic interpolation both in space and time. We also propose an H.254/AVC compatible video space-time SR framework by using learned CDCA, which enables to super-resolved compressed LR video with less computational complexity. The experimental results prove that the proposed H.264/AVC compatible framework performs better than the state-of-art techniques on space-time SR in terms of quality and time complexity.
|
|
| Date |
2019-11-28
|
|
| Type |
Conference or Workshop Item
PeerReviewed |
|
| Format |
application/pdf
|
|
| Identifier |
http://ceeri.csircentral.net/317/1/12-2017.pdf
Sharma, M and Chaudhary, S and Lall, B (2019) Space-Time Super-Resolution using Deep Learning-based Framework. In: 7th International Conference on Pattern Recognition and Machine Intelligence, December 5-8, 2017, Kolkata. (Submitted) |
|
| Relation |
http://ceeri.csircentral.net/317/
|
|