Earticle

초록

영어

In this research paper, theoretical analysis and applications of a novel single image super-resolution algorithm through regularization approach and joint learning is introduced. Digital image during the process of obtaining the optical fuzzy, movement deformation and degradation factors such as random noise, the influence of the resulting often degradation image, sometimes its resolution is difficult to meet the actual demand of engineering or military applications. In this paper, we combine the joint learning theory together with the regularization standard, through parameter selection, error estimation with omission and solution analysis steps. The proposed framework is based on modified super-resolution model and novel error estimation metrics. In the experiment section, we compare our proposed algorithm with other state-of-the-art and popularly adopted methodologies and use the well-known test image databases to conduct the experiment. The experimental result shows the feasibility and effectiveness of the algorithm. In the future, we plan to do more in-depth research on the parameter selection part to modify our method.

목차

Abstract
 1. Introduction
 2. The Image Super-resolution Model and Error Estimation
  2.1. The Image Super-resolution Model
  2.2. The Error Estimation Technique
 3. Tradition Regularization and Joint Learning Algorithm
  3.1. Weakness of Tradition Regularization Method
  3.2. Joint Learning Algorithm
 4. Our Proposed Model
  4.1. Overview of Our Model
  4.2. The Adaptive of the Prior Model
  4.3. The Parameter Selection for the Regularization Model
  4.4. The Solution for the Regularization Model
 5. Experiment and Simulation
  5.1. The Experiment Set-up and Initiation
  5.2. The Experimental Result
 6. Conclusion and Summary
 References

키워드

저자

• Fengling Yin [ Binzhou Polytechnic, Shandong, China ]
• Bingquan Huo [ Binzhou Polytechnic, Shandong, 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