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Introduction

The visibility of outdoor images is often compromised by the presence of haze, which leads to degradation in image quality. Image dehazing, the process of removing haze from images, has attracted significant attention in the field of computer vision and image processing. Traditional methods for dehazing rely on handcrafted features and assumptions about the scene, which limit their effectiveness in challenging scenarios.

Deep learning, a subfield of artificial intelligence, has shown great potential in various image processing tasks including image dehazing. By learning hierarchical representations of data, deep learning models can effectively capture complex relationships within images and generate high-quality dehazed results. This thesis aims to investigate the application of deep learning techniques for image dehazing and to explore novel approaches to improve the quality of dehazed images.

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 dehazing
2.2 Traditional image dehazing methods
2.3 Deep learning for image dehazing
2.4 Recent advances in image dehazing using deep learning
2.5 Evaluation metrics for image dehazing
2.6 Challenges in image dehazing using deep learning
2.7 Transfer learning in image dehazing
2.8 Domain adaptation in image dehazing
2.9 Dataset selection for image dehazing
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Introduction to research methodology
3.2 Data collection and preprocessing
3.3 Model architecture selection
3.4 Training process
3.5 Hyperparameter optimization
3.6 Evaluation metrics
3.7 Experiment design
3.8 Cross-validation techniques
3.9 Performance analysis
3.10 Summary of research methodology

Chapter 4: Discussion of Findings
4.1 Introduction to findings
4.2 Performance comparison with existing methods
4.3 Analysis of experimental results
4.4 Interpretation of model behavior
4.5 Generalization capabilities of the model
4.6 Limitations and future work
4.7 Potential applications of the proposed method
4.8 Impact of the findings
4.9 Discussion with related works
4.10 Summary of findings

Chapter 5: Conclusion and Summary
5.1 Conclusion
5.2 Contributions of the thesis
5.3 Implications of the research
5.4 Recommendations for future research
5.5 Summary of the thesis

Thesis Overview on Image Dehazing Using Deep Learning

Image dehazing is a fundamental task in computer vision that aims to enhance the visual quality of images by removing unwanted haze and improving visibility. Traditional methods for dehazing rely on handcrafted features and assumptions about scene properties, which often limit their effectiveness in challenging scenarios. In recent years, deep learning has emerged as a powerful tool for image processing tasks, including image dehazing. By learning hierarchical representations of data, deep learning models can effectively capture complex relationships within images and generate high-quality dehazed results.

This thesis focuses on the application of deep learning techniques for image dehazing and explores novel approaches to improve the quality of dehazed images. The research methodology involves data collection and preprocessing, model architecture selection, training process, hyperparameter optimization, evaluation metrics, experiment design, cross-validation techniques, and performance analysis. The findings are discussed in detail, including performance comparison with existing methods, analysis of experimental results, interpretation of model behavior, generalization capabilities, limitations, and future work.

In conclusion, this thesis makes significant contributions to the field of image dehazing using deep learning. The proposed method shows promising results in enhancing image quality and visibility, with potential applications in various domains. Recommendations for future research include exploring different model architectures, improving dataset quality, and investigating transfer learning and domain adaptation techniques. This thesis provides a comprehensive overview of image dehazing using deep learning and sets the foundation for future research in this exciting field.

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