지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2014.2
- 수록면
- 1 - 14 (14page)
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초록· 키워드
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The major goal of a super-resolution image reconstruction method is to construct a single in-depth high-resolution image from a group of numerous low-resolution images of the scene taken from diverse positions. Since every low-resolution image retains a different view of the scene, it is possible to reconstruct an in-depth high-resolution image. Thus image super-resolution is a key to overcoming the material precincts of hardware competence. An enormous amount of video is still in traditional formats or at an even lower resolution. Some also has relentless coding artifacts. Hence, there is a need for techniques that can improve video quality and that show all the traditional and low-resolution videos on panels with high-resolution grids. Iterative super-resolution reconstruction algorithms can accomplish this exigent chore by using internal image models and an incorporated feedback loop to control output quality, thereby improving resolution and lessening artifacts. This article portrays the prospects of iterative reconstruction algorithms and establishes a new super-resolution algorithm that is computationally very strong and efficient against motion estimation errors.
목차
Abstract
Introduction
Previous Research
Proposed Research Method
Mathematical Formulation of the Problem
Presented Enhancements
Feature Analysis
Experimental Results
References
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UCI(KEPA) : I410-ECN-0101-2015-500-002466766