Efficient Denoising Algorithms for Intelligent Recognition Systems

A.L. Priorov, K.I. Tumanov, V.A. Volokhov

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

Abstract

A great variety of electronic devices nowadays provide images with different quality, resolution, and color depth parameters. Despite of the differences, all intelligent recognition systems built from the basic image capturing components inevitably involve image preprocessing blocks, which in addition to other tasks perform the “raw” image filtration. This chapter presents the algorithms of modern filtration techniques dealing with the Principal Component Analysis (PCA) and nonlocal processing based on the denoising algorithms including multiple examples. Some attention is devoted to the modeling of noise on raw images. Finally, the charter ends with a discussion on the applicability of the specific filtration algorithms to modern tasks such as pattern recognition and object tracking.

Efficient Denoising Algorithms for Intelligent Recognition Systems. Available from: https://www.researchgate.net/publication/282289136_Efficient_Denoising_Algorithms_for_Intelligent_Recognition_Systems [accessed Mar 06 2018].
Original languageEnglish
Title of host publicationComputer Vision in Control Systems-2: Innovations in Practice
PublisherSpringer
Chapter10
Pages251-276
Number of pages26
Volume75
ISBN (Electronic)978-3-319-11430-9
ISBN (Print)978-3-319-11429-3
Publication statusPublished - 2015

Keywords

  • image filtering
  • Principal Component Analysis
  • Non-local processing
  • Denoising algorithm
  • Image reconstruction
  • Image recognition

Cite this

Priorov, A. L., Tumanov, K. I., & Volokhov, V. A. (2015). Efficient Denoising Algorithms for Intelligent Recognition Systems. In Computer Vision in Control Systems-2: Innovations in Practice (Vol. 75, pp. 251-276). Springer.