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Signal, Image and Video Processing

, Volume 13, Issue 7, pp 1347–1355 | Cite as

Power shrinkage—curvelet domain image denoising using a new scale-dependent shrinkage function

  • Oussama KadriEmail author
  • Zine-Eddine Baarir
  • Gerald Schaefer
Original Paper
  • 75 Downloads

Abstract

Image processing and analysis algorithms are at the heart of applications in various scientific fields, such as medical diagnosis, military imaging, and astronomy. However, images are typically exposed to noise contamination during their acquisition and transmission. In this paper, we explore recent advancements in image denoising using curvelet domain shrinkage and present a novel scale-dependent shrinkage function, which we call power shrinkage, to enhance restored image quality. Experimental results confirm our proposed method to perform better than classical thresholding and to outperform recent state-of-the-art approaches in denoising different types of noises including speckle, Poisson and additive white Gaussian noise.

Keywords

Image noise Image quality Image denoising Curvelet shrinkage Power shrinkage 

Notes

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.LESIA Laboratory of research, Electrical Engineering DepartmentMohammed Khider UniversityBiskraAlgeria
  2. 2.Department of Computer ScienceLoughborough UniversityLoughboroughUK

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