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- Title
A robust facial image super-resolution model via mirror-patch based neighbor representation.
- Authors
Rajput, Shyam Singh; Arya, K. V.
- Abstract
Many patch-based facial image super-resolution (or hallucination) techniques have been proposed in literature but all of them fail in the presence of high-density impulse noise and occlusion. A novel mirror-patch based neighbor representation (MPNR) model is proposed here which uses mirror-patch based data fidelity along with the input-patch based fidelity in low-resolution (LR) space to address the above problem. The computation of mirror-patch based data fidelity helps in compensating the corrupted features of an input patch through its mirror-patch. The objective function of the proposed model is designed in such a way that it hallucinate the input LR faces and takes care of occlusion/heavy noise effect simultaneously in the reconstruction process. It is conspicuous from experimental results attained on FEI and CAS-PEAL-R1 databases that the proposed MPNR model has outperformed all the comparative methods.
- Subjects
HIGH resolution imaging; BURST noise; COMPARATIVE method; IMAGE reconstruction algorithms; ILLUSION (Philosophy)
- Publication
Multimedia Tools & Applications, 2019, Vol 78, Issue 18, p25407
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-019-07791-y