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Raspberry Pi Computer Vision Programming

You're reading from   Raspberry Pi Computer Vision Programming Design and implement computer vision applications with Raspberry Pi, OpenCV, and Python 3

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781800207219
Length 306 pages
Edition 2nd Edition
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Author (1):
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Ashwin Pajankar Ashwin Pajankar
Author Profile Icon Ashwin Pajankar
Ashwin Pajankar
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Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Introduction to Computer Vision and the Raspberry Pi 2. Chapter 2: Preparing the Raspberry Pi for Computer Vision FREE CHAPTER 3. Chapter 3: Introduction to Python Programming 4. Chapter 4: Getting Started with Computer Vision 5. Chapter 5: Basics of Image Processing 6. Chapter 6: Colorspaces, Transformations, and Thresholding 7. Chapter 7: Let's Make Some Noise 8. Chapter 8: High-Pass Filters and Feature Detection 9. Chapter 9: Image Restoration, Segmentation, and Depth Maps 10. Chapter 10: Histograms, Contours, and Morphological Transformations 11. Chapter 11: Real-Life Applications of Computer Vision 12. Chapter 12: Working with Mahotas and Jupyter 13. Chapter 13: Appendix 14. Other Books You May Enjoy

Restoring damaged images using inpainting

The restoration of an image is the computational process of reconstructing damaged parts from existing parts of an image. If we capture an image on film with a photographic camera and develop it on paper, the photographic paper tends to degrade with the passage of time, leading to degradation of the photograph. Faulty sensors and imperfections such as dust and dirt on the camera lenses can introduce errors in the captured image. The process of transmission and reception can also introduce errors in the digital image. Image inpainting techniques can restore degraded and damaged images. Many algorithms are available to repair images. The OpenCV library implements two of the repairing methods using the cv2.inpaint() function.

This function accepts a degraded or damaged source image, a mask for image inpainting, the size of the inpainting neighborhood, and the inpainting method as arguments. The mask of inpainting is the damaged area represented...

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