Earticle

초록

영어

This paper introduces a novel solution to generate a super-resolution image from a set of low-resolution input based on patch information. Recent research has shown that super-resolved data can be reconstructed from an extremely small set of measurements compared to that currently required. This paper incorporates the compressive sensing framework to the reconstruction model. Moreover, in order to remove outliers introduced by image parallax, the supervised patch-adaptive matching method which uses photometrical similarity and geometrical distance to determine the matching patch is proposed to reconstruct the high resolution image. The performance of the proposed algorithm on both synthetic and real images is evaluated with several grayscale and color image sequences and found successful when compared to other algorithms.

목차

Abstract
 1. Introduction
 2. Compressive Sensing Model
  2.1. Sparse Reconstruction Condition
  2.2. Greedy Reconstruction Algorithm
 3. Proposed Approach
  3.1. Patch Adaptive Super Resolution
  3.2. High Resolution Image Reconstruction
 4. Experimental Results
 5. Conclusion
 References

키워드

저자

• Haitian Zhai [ Department of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China ] Corresponding Author
• Hui Li [ Department of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China ]
• Weiting Gao [ Department of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605