Earticle

초록

영어

We introduce a fast and effective algorithm extreme learning machine (ELM) and apply it to image denoising. GA-ELM algorithm we proposed uses genetic algorithm(GA) to decide weights and bias in the ELM. It has better global optimal characteristics than traditional optimal ELM algorithm. In this paper, we used GA-ELM to do image denosing researching work. Firstly, this paper uses training samples to train GA-ELM as the noise detector. Then, we utilize the well-trained GA-ELM to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-ELM. Experiment data shows that this algorithm has better performance than other denosing algorithm.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Noise Model
  2.2. Noise Model
 3. Our work
  3.1. GA-ELM
  3.2. Denoising Algorithm
  3.3. Workflow
 4. Experiment
 5. Conclusions
 References

키워드

저자

• Zhiyong Fan [ Nanjing University of Science & Technology, Nanjing 210000, China, Nanjing University of Information Science & Technology, Nanjing 210044, ]
• Quansen Sun [ Nanjing University of Science & Technology, Nanjing 210000, China ]
• Feng Ruan [ Nanjing University of Information Science & Technology, Nanjing 210044, China ]
• Kai Hu [ Nanjing University of Information Science & Technology, Nanjing 210044, China ]
• Jin Wang [ Nanjing University of Information Science & Technology, Nanjing 210044, 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