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Chapter 6 Image inpainting using GAN transformerbased model

  • Melika Abdollahi , Aref Abedjooy , Heidar Davoudi and Mehran Ebrahimi
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Imaging Science
This chapter is in the book Imaging Science

Abstract

In computer vision, image inpainting plays a crucial role in restoring missing or damaged areas of an image. Deep learning techniques, especially generative adversarial networks (GANs), have significantly improved image inpainting and image-to-image translation tasks over the past few years. This work examines a novel approach to image inpainting based on a GAN and transformers. The proposed model leverages the advantages of GANs and transformer architectures to improve the quality of generated visual content. Traditional inpainting schemes often struggle to capture complex contextual relationships within the images. We aim to evaluate the feasibility of integrating GANs and transformer-based networks for image inpainting. Furthermore, we extend the model to include image translation followed by the obtained inpainting result using the proposed model.

Abstract

In computer vision, image inpainting plays a crucial role in restoring missing or damaged areas of an image. Deep learning techniques, especially generative adversarial networks (GANs), have significantly improved image inpainting and image-to-image translation tasks over the past few years. This work examines a novel approach to image inpainting based on a GAN and transformers. The proposed model leverages the advantages of GANs and transformer architectures to improve the quality of generated visual content. Traditional inpainting schemes often struggle to capture complex contextual relationships within the images. We aim to evaluate the feasibility of integrating GANs and transformer-based networks for image inpainting. Furthermore, we extend the model to include image translation followed by the obtained inpainting result using the proposed model.

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/10.1515/9783111436425-006

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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