Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Image Processing with Python

You're reading from   Hands-On Image Processing with Python Expert techniques for advanced image analysis and effective interpretation of image data

Arrow left icon
Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789343731
Length 492 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Sandipan Dey Sandipan Dey
Author Profile Icon Sandipan Dey
Sandipan Dey
Arrow right icon
View More author details
Toc

Table of Contents (20) Chapters Close

Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
1. Getting Started with Image Processing 2. Sampling, Fourier Transform, and Convolution FREE CHAPTER 3. Convolution and Frequency Domain Filtering 4. Image Enhancement 5. Image Enhancement Using Derivatives 6. Morphological Image Processing 7. Extracting Image Features and Descriptors 8. Image Segmentation 9. Classical Machine Learning Methods in Image Processing 10. Deep Learning in Image Processing - Image Classification 11. Deep Learning in Image Processing - Object Detection, and more 12. Additional Problems in Image Processing 1. Other Books You May Enjoy Index

Linear noise smoothing


Linear (spatial) filtering is a function with a weighted sum of pixel values (in a neighborhood). It is a linear operation on an image that can be used for blurring/noise reduction. Blurring is used in pre-processing steps; for example, in the removal of small (irrelevant) details. A few popular linear filters are the box filter and the Gaussian filter. The filter is implemented with a small (for example, 3 x 3) kernel (mask), and the pixel values are recomputed by sliding the mask over the input image and applying the filter function to every possible pixel in the input image (the input image's center pixel value corresponding to the mask is replaced by the weighted sum of pixel values, with the weights from the mask). The box filter (also called the averaging filter), for example, replaces each pixel with an average of its neighborhood and achieves a smoothing effect(by removing sharp features; for example, it blurs edges, whereas spatial averaging removes noise...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at AU $24.99/month. Cancel anytime