Fast model searching and combining for example learning-based super-resolution

Chun Wei Chen, Fang Kai Hsu, Der Wei Yang, Jonas Wang, Ming Der Shieh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Single-image super-resolution is an important technique for high resolution display related applications. Example learning-based approaches can provide plenty of image details by using trained dataset. The regression based methods reduce the memory storage size by training mapping functions rather than using a huge dictionary. However, the speed of searching the nearest cluster for the desired mapping function is still the bottleneck of the system. This problem is getting critical when the number of mapping functions is increased. This work presents an operator denoted as local multi-gradient level pattern to fast yet effectively describe the patch local geometry for a cluster of patches. The corresponding cluster can then be quickly identified by a simple lookup table. Furthermore, the potential cluster misclassification problem, induced by adopting the simplified clustering feature, is relaxed by applying the proposed model combining scheme. Simulation results show that the proposed one can achieve about 8 times speedup with even higher SSIM as compared to the related k-mean based method.

Original languageEnglish
Title of host publicationISCAS 2016 - IEEE International Symposium on Circuits and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1994-1997
Number of pages4
ISBN (Electronic)9781479953400
DOIs
Publication statusPublished - 2016 Jul 29
Event2016 IEEE International Symposium on Circuits and Systems, ISCAS 2016 - Montreal, Canada
Duration: 2016 May 222016 May 25

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2016-July
ISSN (Print)0271-4310

Other

Other2016 IEEE International Symposium on Circuits and Systems, ISCAS 2016
CountryCanada
CityMontreal
Period16-05-2216-05-25

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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