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Introduction

Image deblurring is an essential task in the field of computer vision and image processing. It aims at recovering sharp and clear images from blurry input images that are affected by motion blur, defocus blur, or other factors. Traditional methods for image deblurring often rely on hand-crafted features and assumptions about the blur kernel, which limit their adaptability to different types of blur and image content.

In recent years, deep learning has shown remarkable success in various computer vision tasks, including image deblurring. Deep learning-based approaches learn to directly map a blurry image to a sharp image without relying on explicit modeling of the blur kernel. This allows for more flexible and accurate image deblurring, making it one of the most promising areas of research in image processing.

This thesis focuses on exploring the use of deep learning for image deblurring. We aim to investigate different deep learning architectures and frameworks for image deblurring, analyze their performance, and propose novel methods to improve the accuracy and efficiency of image deblurring using deep learning techniques.

The remainder of this thesis is structured as follows:

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Introduction to image deblurring
2.2 Traditional methods for image deblurring
2.3 Deep learning for image deblurring
2.4 Convolutional neural networks for image deblurring
2.5 Generative adversarial networks for image deblurring
2.6 Transfer learning for image deblurring
2.7 Evaluation metrics for image deblurring
2.8 Challenges in image deblurring using deep learning
2.9 Recent advances in image deblurring research
2.10 Gaps in existing literature

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 Model architecture selection
3.5 Training procedure
3.6 Hyperparameter tuning
3.7 Evaluation methodology
3.8 Performance metrics
3.9 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Performance analysis of deep learning models
4.2 Comparison with traditional methods
4.3 Impact of dataset size on model performance
4.4 Generalization to different types of blur
4.5 Computational efficiency of deep learning models
4.6 Interpretability of deep learning models
4.7 Limitations of the proposed methods
4.8 Future directions for research

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions of the study
5.3 Implications for practice
5.4 Recommendations for future research
5.5 Conclusion

Thesis Overview: Image Deblurring Using Deep Learning

Image deblurring is a fundamental problem in image processing and computer vision, with applications in various domains such as photography, surveillance, and medical imaging. Traditional methods for image deblurring often rely on hand-crafted features and assumptions about the blur kernel, which limits their adaptability to different types of blur and image content. In recent years, deep learning has shown significant promise in addressing the challenges of image deblurring by learning complex patterns directly from data.

This thesis aims to investigate the use of deep learning techniques for image deblurring and propose novel methods to improve the accuracy and efficiency of image deblurring processes. The research methodology includes data collection, preprocessing, model selection, training, and evaluation using various performance metrics. The study will compare deep learning-based approaches with traditional methods, analyze the impact of dataset size and blur type on model performance, and explore the interpretability of deep learning models for image deblurring.

The findings of this thesis are expected to contribute to the existing knowledge in the field of image deblurring using deep learning and provide insights for future research. The implications of the study for practice include the development of more accurate and efficient image deblurring algorithms that can be applied in real-world scenarios. Recommendations for future research include investigating the generalization of deep learning models to different types of blur and exploring new architectures for image deblurring.

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