Artificial Intelligence for Image Super Resolution

By Debmitra Ghosh

This book is based upon role of Artificial Intelligence for Image Super Resolution. Here we have various methodology for image processing and signal processing. This book covers basic information about Machine Learning and Deep Learning also.

• Language: English
• Publisher: Debmitra Ghosh
• Release date: May 31, 2023
• ISBN: 9798223613428

Book preview

Artificial Intelligence Application for Image Super Resolution

By Debmitra Ghosh

INDEX

Preface..................................................................................................................................... 3

Acknowledgments.................................................................................................................. 6

Author ..................................................................................................................................... 7

1 Introduction .........................................................................................................................8

2 Adaptive Polynomial Image Interpolation.......................................................................40

3 Neural Modeling of Polynomial Image Interpolation .....................................................68

4 ColorImageInterpolation....................................................................................................90

5 ImageInterpolationforPatternRecognition.....................................................................104

Appendix A: DL for Single Image Super-Resolution: A Brief Review  .................................................................................................................................138

Appendix B: Toeplitz-to-Circulant Approximations...........................................................

Appendix C: Newton’s Method.......................................................................................

Preface

This book offers several novel aspects and contributions in the field of image processing. Here is a summary of the key points:

High-resolution image generation: The book focuses on the challenge of obtaining high-resolution images from single or multiple low-resolution images. This is a significant problem in image processing, and the book explores various techniques and approaches to address it.

Integration of image interpolation and super-resolution: Unlike other resources that treat image interpolation and super-resolution as separate topics, this book presents interpolation as a fundamental component within the super-resolution reconstruction process. By establishing a connection between the two, it offers a more comprehensive understanding of the overall image enhancement process.

Comparison of different approaches: The book compares and contrasts the two major trends in image interpolation—polynomial-based methods and inverse problem-based approaches. By presenting this comparison, readers gain insights into the strengths, weaknesses, and suitability of each approach for different scenarios.

Image registration and fusion: Two dedicated chapters in the book cover the complementary steps of image registration and image fusion. These steps are essential in achieving high-resolution images and are extensively discussed, providing readers with in-depth knowledge and practical techniques.

Two directions for image super-resolution: The book introduces two distinct directions for image super-resolution—super-resolution with a priori information and blind super-resolution. Each approach tackles the problem from a different angle and provides readers with a comprehensive understanding of the possibilities and limitations of both methods.

Applications in medical and satellite image processing: The book showcases the practical applications of image interpolation and super-resolution in the domains of medical and satellite image processing. By highlighting these specific areas, readers gain insights into how the discussed techniques can be applied to real-world scenarios and understand the potential impact of the presented methods.

In summary, this book offers a unique perspective by integrating image interpolation and super-resolution, comparing different approaches, addressing image registration and fusion, presenting multiple directions for super-resolution, and demonstrating applications in medical and satellite image processing. These aspects distinguish it from existing resources in the field of image processing.                                  

Acknowledgments

I would like to thank Dr. Bhabesh Bhattacharjee, Vice Chancellor of JIS University, Dr. Dharmpal Singh, HOD of CSE Department, and School of Engineering for providing me the opportunity for publishing this book. I would also like to thank my family Mr. Asit Baran Ghosh, Mrs. Ajanta Ghosh, Daibik Ghosh Basak and Dakshika Ghosh Basak for their contributions during the preparation of this book.

Author      

Debmitra Ghosh is currently working as Assistant Professor in Computer Science Department of JIS University. She was a Test Automation Engineer and Test Analyst with 8+ years of experience in BAT (Business Analysis and Testing) practice. Experienced in Performance, Automation Testing, Data Migration Testing, API testing & End to End functional testing. She has worked in both Traditional (Waterfall, V-Model) and Agile (Scrum) development environments. She has held various roles as the Test Lead responsible for Test (QA) Management, onsite/offshore Test coordinator, Business Analyst. She has worked in different domains and served clients in the Financial/Accounting, Retail, Insurance, and Audit sector. She has experience in analyzing Business Requirements, Gap Analysis in the As-Is process and coming up with To-Be guidelines, Defining and Implementing functional test strategy and methodology across applications and technology in an integrated and global environment, Implementing Test Policy and produce and use metrics in the test domain to monitor and improve team’s performance, quality of delivered business applications. She managed Test Planning, Preparation and Execution across multiple projects with team sizes ranging from 5 to 10. She has experience in producing regular status reports, verbal and written on the progress of testing activities to the project managers. Her current research focus is on medical imaging analysis using Deep-learning based approach.

Chapter 1

Introduction

The sensor and affects its performance. Therefore, increasing the chip size is not a feasible solution to achieve higher resolution levels. To overcome these limitations, various image processing techniques have been developed to enhance the resolution of low-resolution images. These techniques are collectively known as image super-resolution (SR) methods. The goal of SR is to reconstruct a high-resolution image from one or more low-resolution input images. There are different approaches to image super-resolution, including single-image super-resolution and multi-image super-resolution. Single-image super-resolution methods aim to increase the resolution of a single low-resolution image by exploiting the inherent information within the image itself. These methods utilize techniques such as interpolation, edge enhancement, and statistical modeling to estimate the missing high-frequency details. Multi-image super-resolution methods, on the other hand, utilize multiple low-resolution images of the same scene to extract additional information and improve the resolution. These methods take advantage of the fact that different low-resolution images may capture different details or perspectives of the scene. By aligning and combining multiple low-resolution images, it is possible to enhance the resolution and generate a high-resolution output. Some common techniques used in super-resolution include bicubic interpolation, Lanczos interpolation, wavelet-based methods, and deep learning-based approaches. Deep learning methods, particularly convolutional neural networks (CNNs), have shown remarkable performance in image super-resolution tasks by learning the mapping between low-resolution and high-resolution image patches. It's important to note that while super-resolution techniques can enhance the perceived resolution of an image, they cannot create new information that was not present in the original low-resolution image. The results of super-resolution methods depend on the quality and characteristics of the input images, as well as the specific algorithm used. In summary, due to the limitations of current image acquisition devices in terms of resolution and cost, image super-resolution methods have emerged as a viable solution to enhance the resolution of low-resolution images. These methods utilize various techniques to reconstruct high-resolution details and have found applications in a wide range of fields, including medical imaging, satellite imaging, and high-definition television. The integration of hardware and software capabilities is indeed a promising approach to achieve the desired high-resolution (HR) levels. By utilizing the maximum HR level available from the hardware, a part of the task is accomplished. The remaining task can be handled using software-based image processing algorithms. This approach reflects the current trend in modern image capturing devices. Image interpolation is a technique used in image processing to obtain an HR image from a single low-resolution (LR) image. It involves estimating the missing high-frequency details and increasing the resolution based on the available information. Interpolation methods can be based on various principles, such as nearest-neighbor interpolation, bilinear interpolation, bicubic interpolation, or more advanced algorithms like Lanczos interpolation. These techniques use mathematical algorithms to fill in the gaps and enhance the perceived resolution of the image. On the other hand, image super-resolution refers to the process of generating an HR image from multiple degraded observations of the same scene. This technique takes advantage of the fact that different LR images may capture different details or perspectives. By aligning and combining these images, it becomes possible to extract additional information and improve the overall resolution. It's worth noting that image super-resolution methods can achieve better results compared to single-image interpolation techniques. Super-resolution algorithms can utilize the complementary information present in multiple LR images to enhance the resolution and generate a more accurate representation of the HR scene. These methods often involve complex computational models, including statistical analysis, optimization algorithms, or machine learning techniques, such as deep neural networks. In conclusion, integrating hardware capabilities with software-based image processing algorithms is a practical approach to achieve high-resolution levels. Image interpolation and super-resolution techniques are important tools in this context, allowing the enhancement of resolution and extraction of fine details from LR images. The combination of hardware and software advancements continues to drive the development of more effective solutions for obtaining HR images in various applications.

1.1 Image Interpolation

Image interpolated images can then be used for pattern recognition tasks. This application of polynomial image interpolation in pattern recognition. Overall, polynomial image interpolation techniques involve estimating missing pixels in an LR image by inserting interpolated pixels using polynomial expansions. Spline interpolation is one popular algorithm used in polynomial image interpolation. However, traditional polynomial interpolation algorithms do not take into account the specific degradation model of the LR image, limiting their performance. Adaptive variants of polynomial image interpolation have been proposed to improve the interpolation results. Some adaptive methods focus on distance adaptation without considering the LR image degradation model, while others consider the degradation model to achieve better interpolation performance. Color image interpolation is a specific application that addresses the interpolation of missing color components in a digital imaging process. Since not all color components may be available in the acquired image, interpolation is needed to estimate the missing components based on the existing components of neighboring pixels. In addition to its applications in image enhancement, polynomial image interpolation has also found utility in pattern recognition. By reducing the sizes of database images through decimation, polynomial image interpolation can be used to restore the images to their original sizes during the recognition step. The interpolated