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Introduction:

Image denoising, the process of removing noise from images, is a fundamental task in the field of image processing. Over the years, various methods have been developed to address this issue, with deep learning algorithms proving to be highly effective in recent years. Deep learning models have shown great potential in learning complex features from data, making them suitable for tasks such as image denoising.

This thesis focuses on exploring the use of deep learning techniques for image denoising. Specifically, we aim to develop a deep learning model that can effectively remove noise from images while preserving important image details. The goal is to improve the overall image quality and make images more visually appealing.

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 denoising
2.2 Traditional image denoising methods
2.3 Deep learning in image processing
2.4 Deep learning for image denoising
2.5 Convolutional neural networks
2.6 Autoencoders
2.7 Generative adversarial networks
2.8 Performance evaluation metrics
2.9 Recent advancements in image denoising
2.10 Gaps in existing literature

Chapter 3: Research Methodology
3.1 Introduction
3.2 Data collection
3.3 Preprocessing
3.4 Model architecture
3.5 Training process
3.6 Hyperparameter tuning
3.7 Evaluation metrics
3.8 Experimental setup

Chapter 4: Discussion of Findings
4.1 Introduction
4.2 Performance evaluation
4.3 Comparison with existing methods
4.4 Analysis of results
4.5 Model limitations
4.6 Future research directions

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions of the study
5.3 Practical implications
5.4 Recommendations for future work
5.5 Conclusion

Thesis Overview on Image Denoising Using Deep Learning:

Image denoising is a critical task in image processing that aims to remove unwanted noise from images while preserving important image details. Traditional methods for image denoising have limitations in dealing with complex noise patterns and often result in loss of image quality. In recent years, deep learning techniques have shown great promise in addressing this issue by learning complex features directly from data.

This thesis focuses on exploring the use of deep learning algorithms for image denoising. The main objective is to develop a deep learning model that can effectively remove noise from images and improve overall image quality. The research methodology includes data collection, preprocessing, model architecture design, training process, hyperparameter tuning, and performance evaluation using various metrics.

The literature review covers traditional image denoising methods, deep learning techniques in image processing, convolutional neural networks, autoencoders, and generative adversarial networks. The discussion of findings includes performance evaluation, comparison with existing methods, analysis of results, model limitations, and future research directions.

Overall, this thesis aims to contribute to the field of image denoising using deep learning techniques and provides valuable insights for researchers and practitioners working in the field of image processing.

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