AN IMPROVED TECHNIQUE FOR MIX NOISE AND BLURRING REMOVAL IN DIGITAL IMAGES

By UTKARSH SHUKLA

One or many image sensor develops a digital image. Along with different kinds of light-sensitive cameras, it also incorporates a range of sensors, radar, tomography devices, ultra-sonic cameras, and so on. This book is helpful for undergraduate, postgraduate students, research scholars who belong to the field of image processing and engineering.

• Language: English
• Publisher: Blue Rose Publishers
• Release date: Mar 18, 2020
• ISBN: 9789390034307

Book preview

AN IMPROVED

TECHNIQUE

FOR MIX

NOISE AND

BLURRING

REMOVAL IN

DIGITAL IMAGES

Utkarsh Shukla

© Utkarsh Shukla 2020

All rights reserved

All rights reserved by author. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the author.

Although every precaution has been taken to verify the accuracy of the information contained herein, the author and publisher assume no responsibility for any errors or omissions. No liability is assumed for damages that may result from the use of information contained within.

First Published in March 2020

ISBN: 978-93-90034-30-7

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Cover Design:

Tyngshain Pariat

Typographic Design:

Namrata Saini

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Table of Contents

Chapter 1 Digital Image Processing

Introduction

Fundamental in Digital Image Processing

Image Pre-Treatment

Gray Level

Binary Level

Histogram Distribution

Histogram Equalization

Denoising

De-Blurring

Statement of Problem

Chapter 2 Image Noises and Removal

Image Processing And Noise Filtering

Noise Types

Gaussian Blur

Filters

Nonlinear Filters

Various Image Restoration Filters

Conclusions

Chapter 3 Problem Formulation and G-Tv Model

Introduction

Rudin-Osher-Fatemi (ROF) Model

Gauss-Total Variation Model (G-Tvmodel)

Conclusions

Chapter 4 Gaussian Mixture-Total Variation Model (Gm-Tv Model)

Introduction

Gm-Tv Model

Results

Conclusion

Chapter 5 Conclusions and Future Works

Conclusions

Future Works

Refrences

Dedication

For my Family and Teachers.

Preface

For the past recent decades, Image de noising has been analyzed in many fields such as computer vision, statistical signal and image processing. It facilitates a appropriate base for the analysis of natural image models and signal separation algorithms. Moreover, it also turns into an essential part to digital image acquiring systems to improve qualities of image. These two directions are vital and will be examined in this work.

In video and still image camera noise and blurring of images is often seen. Image noises can be removed using various classes of filters. In case of mixed noises filter cannot eliminate noise completely. To remove such noises pdf estimation of noises becomes important. Blurring of images is another degrading factor and when image is corrupted with both blurring and mixed noises de-noising and de-blurring of image is very difficult. In this book, Gauss-Total Variation model (G-TV model) is discussed and results are presented and it is shown that blurring of image is completely removed using G-TV model, however, image corrupted with blurring and mixed noise cannot be completely recovered.

Acknowledgement

On the completion of this book entitled "AN IMPROVED APPORACH FOR MIX NOISE AND BLURRING REMOVAL IN DIGITAL IMAGES, it is my proud privilege to express my deep sense of gratitude and indebtness to Dr. Rajiv Srivastava, Ex- Faculty Indian institute of Technology Jodhpur for their supervisions and skilled guidance during this work. I feel proud to be enriched by unfailing support, excellent supervision, simulating discussions and constant encouragement .Dr. Srivastava not only during this work but also over entire period of my association with him. I offer my most humble and profound indebtness to Dr.Niraj Singhal, Professor S.U Meerut for their deep concern