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Introduction

Style transfer is a fascinating area of research that aims to transform the style of an image while preserving its content. This technique has been widely used in the field of computer vision and graphics to create artistic rendering, photo editing, and image enhancement. By transferring the artistic style of one image onto another, it is possible to generate visually appealing and creative results.

This thesis aims to explore the various methods and techniques available for style transfer for artistic rendering. The research will discuss the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms related to style transfer for artistic rendering.

Chapter One: 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 Two: Literature Review
2.1 Introduction to Style Transfer
2.2 Neural Style Transfer
2.3 Texture Synthesis
2.4 Style Transfer Techniques
2.5 Deep Learning for Style Transfer
2.6 Applications of Style Transfer
2.7 Evaluation Metrics for Style Transfer
2.8 Challenges in Style Transfer
2.9 Future Trends in Style Transfer

Chapter Three: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Neural Network Models
3.4 Loss Functions for Style Transfer
3.5 Training Process
3.6 Hyperparameter Tuning
3.7 Evaluation Criteria
3.8 Experimental Setup

Chapter Four: System Implementation
4.1 Implementation Overview
4.2 Dataset Selection
4.3 Model Selection
4.4 Training Process
4.5 Testing and Validation
4.6 Results Analysis
4.7 Performance Evaluation
4.8 Comparison with Existing Methods

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Future Directions
5.4 Conclusion

Thesis Overview on Style Transfer for Artistic Rendering

Style transfer is a burgeoning field in computer vision and graphics that involves transferring the artistic style of one image to another while preserving the content. This thesis aims to explore the various techniques and methods available for style transfer for artistic rendering.

The literature review chapter will provide an in-depth analysis of existing research on style transfer, including neural style transfer, texture synthesis, deep learning approaches, and evaluation metrics. It will also discuss the challenges and future trends in the field.

The system design and methodology chapter will outline the system architecture, data collection, neural network models, loss functions, training process, hyperparameter tuning, and evaluation criteria for the study.

The system implementation chapter will delve into the details of dataset selection, model selection, training process, testing and validation, results analysis, performance evaluation, and comparison with existing methods.

Finally, the conclusion and summary chapter will summarize the findings, highlight the contributions of the study, suggest future research directions, and conclude the thesis on style transfer for artistic rendering.

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