RNS-Based Image Processing

  • Nikolay Chervyakov
  • Pavel LyakhovEmail author


In this chapter we consider the features of the Residue Number System application for digital image processing. The tasks of digital image processing are very diverse in their methods of solution. Residue Number System can be effectively used for tasks that require a convolution calculation, which is a combination of addition and multiplication operations. At first we consider the Linear Time-Invariant filters which are used to edge detection, sharpening and smoothing of images. We will consider questions about the sufficient dynamic range of Residue Number System and computations with fractional filter coefficients. Next we describe the features of image processing based on Discrete Wavelet Transform which is used for image denoising and compression. In addition, we describe the use of a Finite Field Wavelets for digital image processing particularly in image encryption with using several finite fields combined in Residue Number System.


Discrete Cosine Transform Discrete Wavelet Transform Number System Digital Image Processing Filter Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.North Caucasus Federal UniversityStavropolRussia

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