Probability weighted moments regularization based blind image De-blurring

  • Hussain DawoodEmail author
  • Hassan Dawood
  • Guo Ping
  • Rashid Mehmood
  • Ali Daud
  • Abdullah Alamri
  • Jalal S. Alowibdi


The main objective of blind image de-blurring is to recover a sharp image from a given blurry image. A good estimation of the kernel plays an important role in recovering a sharp image. However, if the local object textures are neglected when the kernel is being estimated, this can lead to over-smoothing or can produce a strong ringing effect. In this paper, a new image regularization term based on the Probability Weighted Moments (PWM) for kernel estimation is proposed named as Probability Weighted Moments Regularization (PWMR). PWMR has the ability to preserve the small local texture structure in an image while minimizing the artifacts. Further, it can preserve the better contrast information between neighboring pixels and their corresponding central pixels in a current sliding window; moreover, it has the ability to resist outliers even in a small sample size. The kernel estimated by PWMR is subsequently used to recover the sharp latent image. An extensive comparison of synthetic and real standard benchmark images indicates the effectiveness of PWMR compared to current state-of-the-art blind image de-blurring methods.


Blind image de-blurring Image regularization Kernel estimation Probability weighted moments 



This work is fully supported by the grants from the Joint Re-search Fund in Astronomy (Grant No. U1531242) under cooperative agreement between the National Natural Science Foundation of China (NSFC) and Chinese Academy of Sciences (CAS), Prof. Ping Guo is the author to whom all correspondence should be addressed.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer and Network Engineering, College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia
  2. 2.Department of Software EngineeringUniversity of Engineering and TechnologyTaxilaPakistan
  3. 3.School of Systems ScienceBeijing Normal UniversityBeijingPeople’s Republic of China
  4. 4.Department of Software EngineeringUniversity of KotliAzad and Jammu KashmirPakistan
  5. 5.Department of Computer Science & Software EngineeringInternational Islamic UniversityIslamabadPakistan
  6. 6.College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia

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