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Noisy Image Fusion Based on a Neural Network with Linearly Constrained Least Square Optimization

  • Xiaojuan Liu
  • Lidan Wang
  • Shukai Duan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)

Abstract

Image fusion algorithm is a key technology to eliminate noise through combining each image with different weight. Recently, convergence and convergence speed are two exiting problems which attract more and more attention. In this paper, we originally propose a image fusion algorithm based on neural network. Firstly, the linearly constrained least square(LCLS) model which can deal with image fusion problem is introduced. In addition, in order to handle LCLS model, we adopt the penalty function technique to construct a neural network. The proposed algorithm has a simpler structure and faster convergence speed. Lastly, simulation results show this fusion algorithm which has great ability to remove different noise.

Keywords

LCLS Neural network Image fusion algorithm Penalty function 

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  1. 1.School of Electronics and Information EngineeringSouthwest UniversityChongqingP.R. China

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