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Robust Huber similarity measure for image registration in the presence of spatially-varying intensity distortion

Ghaffari, A ; Sharif University of Technology | 2015

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  1. Type of Document: Article
  2. DOI: 10.1016/j.sigpro.2014.10.022
  3. Publisher: Elsevier , 2015
  4. Abstract:
  5. Similarity measure is an important part of image registration. The main challenge of similarity measure is lack of robustness to different distortions. A well-known distortion is spatially-varying intensity distortion. Its main characteristic is correlation among pixels. Most traditional intensity based similarity measures (e.g., SSD, MI) assume stationary image and pixel to pixel independence. Hence, these similarity measures are not robust against spatially-varying intensity distortion. Here, we suppose that non-stationary intensity distortion has a sparse representation in transform domain, i.e. its distribution has high peak at origin and a long tail. We use two viewpoints of Maximum Likelihood (ML) and Robust M-estimator. First, using the ML view, we propose robust Huber similarity measure (RHSM) in spatial transform domain as a new similarity measure in a mono-modal setting. In fact, RHSM is a combination of ℓ2 and ℓ1 norms. To demonstrate robustness of the proposed similarity measure, image registration is treated as a non-linear regression problem. In this view, covariance matrix of estimated parameters is obtained based on the one-step M-estimator. Then with minimizing Fisher information function, robust similarity measure of RHSM is introduced. This measure produces accurate registration results on both artificial as well as real-world problems that we have examined
  6. Keywords:
  7. Non-stationary intensity distortion ; Covariance matrix ; Fisher information matrix ; Image registration ; Maximum likelihood ; Huber norms ; Non-linear regression ; Nonstationary ; Outlier ; Sparseness ; Pixels
  8. Source: Signal Processing ; Volume 109 , April , 2015 , Pages 54-68 ; 01651684 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0165168414004861