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An Improved Multi-resolution image fusion framework using image enhancement technique

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2017, 22(12), pp.69-77
  • DOI : 10.9708/jksci.2017.22.12.069
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 11, 2017
  • Accepted : November 17, 2017
  • Published : December 29, 2017

지호진 1 Chulhee Jang 1 진상훈 1 Yonghee Hong 1

1LIG넥스원

Accredited

ABSTRACT

This paper represents a novel framework for multi-scale image fusion. Multi-scale Kalman Smoothing (MKS) algorithm with quad-tree structure can provide a powerful multi-resolution image fusion scheme by employing Markov property. In general, such approach provides outstanding image fusion performance in terms of accuracy and efficiency, however, quad-tree based method is often limited to be applied in certain applications due to its stair-like covariance structure, resulting in unrealistic blocky artifacts at the fusion result where finest scale data are void or missed. To mitigate this structural artifact, in this paper, a new scheme of multi-scale fusion framework is proposed. By employing Super Resolution (SR) technique on MKS algorithm, fine resolved measurement is generated and blended through the tree structure such that missed detail information at data missing region in fine scale image is properly inferred and the blocky artifact can be successfully suppressed at fusion result. Simulation results show that the proposed method provides significantly improved fusion results in the senses of both Root Mean Square Error (RMSE) performance and visual improvement over conventional MKS algorithm.

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