ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술지 Super Resolution Reconstruction Based on Block Matching and Three-Dimensional Filtering with Sharpening
Cited 4 time in scopus Download 11 time Share share facebook twitter linkedin kakaostory
저자
김유경, 오한, Ali Bilgin
발행일
201512
출처
IET Image Processing, v.9 no.12, pp.1048-1056
ISSN
1751-9659
출판사
IET
DOI
https://dx.doi.org/10.1049/iet-ipr.2014.0566
협약과제
15MR3200, (통합)방송용 영상 인식 기반 객체 중심 지식 융합 미디어 서비스 플랫폼 개발, 조기성
초록
Super resolution (SR) reconstruction is often considered to be an inverse problem in the sense that unknown high resolution images are sought for giving low resolution images. Recent studies have shown that the sparsity regularisation used in compressed sensing (CS) reconstruction improves the performance of SR reconstruction. Furthermore, under the assumption that mutually similar regions exist within a natural image, non-local (NL) estimation produces accurate estimates for given degraded images. The incorporation of this NL estimation in SR reconstruction has been shown to yield better reconstructions. In this study, the authors propose the use of block matching and three-dimensional filtering with sharpening estimation as the regularisation constraint under the CS-based SR framework. This estimation collects similar blocks and adaptively filters them by the shrinkage of the transform coefficients. It recovers detailed structures while attenuating ringing artefacts. In addition, a sharpening technique used in the estimation also emphasises edges. As a result, the proposed SR algorithm searches for the solution that is similar to this enhanced estimate from among all feasible solutions. The experimental results demonstrate that the proposed method provides high-quality SR images, both numerically and subjectively.
KSP 제안 키워드
Block matching, Compressed sensing, Degraded Images, Feasible solution, High-quality, Inverse Problem, Low-resolution images, Non-local, Super-resolution reconstruction, Three dimensional(3D), high resolution image