earticle

논문검색

Variational Bayesian Inference Based Image Inpainting using Gamma Distribution Prior

초록

영어

Variational Bayesian (VB) inference is the latest iterative method for prediction of data in machine learning. It provides the solution for intractable integration in Bayesian methodology. In this paper, a simple VB linear regression is applied for prediction of the damaged pixels in an image. Bayesian linear regression model is used for prediction of the pixels. For this neighbor pixels are used as training data to generate the parameters of the prediction function. Now using this prediction function, damaged pixels are predicted and incorporated into the image. Proposed method is linear while image is a non-linear object, generally. Hence, for linearity, a small image window size is used to avoid the nonlinearities in image.

목차

Abstract
 1. Introduction
 2. Review of Fast Marching Method
 3. Elements of Variational Bayes
 4. Bayesian Linear Regression Model
 5. Variational Approximation and Results
 6. Conclusion
 References

저자정보

  • Rohit Sain Electronics and Communication Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India
  • Vikas Mittal Electronics and Communication Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India
  • Vrinda Gupta Electronics and Communication Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India

참고문헌

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

      0개의 논문이 장바구니에 담겼습니다.