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Chapter 2 Denoising and gradient fusion for effective edge detection for noisy color images

  • Jason Shin , Luke Hand , Penelope J. Prochnow , Seth T. Richey , Owen C. Burton , J. Brittin Perdue and Seongjai Kim
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Imaging Science
This chapter is in the book Imaging Science

Abstract

The Canny algorithm is effective for the edge detection for various gray images, although it is sensitive to noise as most other edge detection algorithms are. The noise removal step often weakens not only noise but also the edge strength. This chapter proposes an innovative denoising operator called the reverse-transition weighting (RTW) filter, which can suppress noise without weakening the edge strength. The RTW filter is analyzed for its stability and adopted for the noise removal step of the Canny algorithm, replacing the conventional Gaussian smoothing filter. For the Canny algorithm to be applied for color images, we also consider and compare gradientfusion methods which combine the RGB gradients into one. The structure tensor method shows satisfactory properties for gradient fusion. Our goal is to formulate a robust edge detection algorithm for color images, particularly for heavily noisy images. Various examples are given to show the effectiveness of the new RTW filter and the structure tensor method for gradient fusion.

Abstract

The Canny algorithm is effective for the edge detection for various gray images, although it is sensitive to noise as most other edge detection algorithms are. The noise removal step often weakens not only noise but also the edge strength. This chapter proposes an innovative denoising operator called the reverse-transition weighting (RTW) filter, which can suppress noise without weakening the edge strength. The RTW filter is analyzed for its stability and adopted for the noise removal step of the Canny algorithm, replacing the conventional Gaussian smoothing filter. For the Canny algorithm to be applied for color images, we also consider and compare gradientfusion methods which combine the RGB gradients into one. The structure tensor method shows satisfactory properties for gradient fusion. Our goal is to formulate a robust edge detection algorithm for color images, particularly for heavily noisy images. Various examples are given to show the effectiveness of the new RTW filter and the structure tensor method for gradient fusion.

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Contents VII
  4. Section: Image processing
  5. Chapter 1 Magnetic resonance image re-parameterization on real data 1
  6. Chapter 2 Denoising and gradient fusion for effective edge detection for noisy color images 17
  7. Chapter 3 Understanding driver attention to objects for ADASs: what do drivers see? 39
  8. Chapter 4 Image clustering enhanced with refined image classification 59
  9. Chapter 5 AI-powered framework for objective scoring of product design innovation 89
  10. Section: Computer vision
  11. Chapter 6 Image inpainting using GAN transformerbased model 111
  12. Chapter 7 Enhanced image watermarking through cross-attention and noise-invariant domain learning 127
  13. Chapter 8 Online melt pool monitoring using a deep transformer image processing solution 153
  14. Chapter 9 Implementation of deep learning techniques on thermal image classification 173
  15. Chapter 10 Drishti: a generative AI-based application for gesture recognition and execution 203
  16. Section: Pattern recognition
  17. Chapter 11 Exploring muzzle biometrics: a deep learning framework for noninvasive cattle recognition 239
  18. Chapter 12 Utilizing real-world data to develop a userindependent sensor-based human activity recognition system 253
  19. Index 273
https://doi.org/10.1515/9783111436425-002

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Contents VII
  4. Section: Image processing
  5. Chapter 1 Magnetic resonance image re-parameterization on real data 1
  6. Chapter 2 Denoising and gradient fusion for effective edge detection for noisy color images 17
  7. Chapter 3 Understanding driver attention to objects for ADASs: what do drivers see? 39
  8. Chapter 4 Image clustering enhanced with refined image classification 59
  9. Chapter 5 AI-powered framework for objective scoring of product design innovation 89
  10. Section: Computer vision
  11. Chapter 6 Image inpainting using GAN transformerbased model 111
  12. Chapter 7 Enhanced image watermarking through cross-attention and noise-invariant domain learning 127
  13. Chapter 8 Online melt pool monitoring using a deep transformer image processing solution 153
  14. Chapter 9 Implementation of deep learning techniques on thermal image classification 173
  15. Chapter 10 Drishti: a generative AI-based application for gesture recognition and execution 203
  16. Section: Pattern recognition
  17. Chapter 11 Exploring muzzle biometrics: a deep learning framework for noninvasive cattle recognition 239
  18. Chapter 12 Utilizing real-world data to develop a userindependent sensor-based human activity recognition system 253
  19. Index 273
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