An Adaptive Window Approach for Image Smoothing and Structures Preserving 1st edition by Charles Kervrann ISBN 3540219828 9783540219828

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ISBN 10:       3540219828
ISBN 13: 978-3540219828
Author:         Charles Kervrann
 

An Adaptive Window Approach for Image Smoothing and Structures Preserving 1st  Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 Challenges in Image Smoothing and Denoising
  • 1.3 Importance of Structure Preservation in Image Processing
  • 1.4 Objectives and Contributions of the Paper
  • 1.5 Organization of the Paper

• Related Work

  • 2.1 Traditional Image Smoothing Techniques
  • 2.2 Edge-Preserving Filters and Methods
  • 2.3 Adaptive Filtering Approaches
  • 2.4 Multi-Scale and Context-Aware Image Processing
  • 2.5 Limitations of Existing Smoothing and Denoising Techniques

• Mathematical Background

  • 3.1 Fundamentals of Image Smoothing
  • 3.2 Gaussian and Non-Gaussian Smoothing Filters
  • 3.3 Edge Detection and Preservation Techniques
  • 3.4 Adaptive Filtering and Window Size Selection
  • 3.5 Image Structures: Edges, Textures, and Other Features

• The Adaptive Window Approach

  • 4.1 Overview of the Adaptive Window Method
  • 4.2 Determining the Optimal Window Size for Smoothing
  • 4.3 Mechanisms for Preserving Structures During Smoothing
  • 4.4 Adaptive Weighting Strategies Based on Local Image Properties
  • 4.5 Relationship Between Window Size and Image Features

• Algorithm Design and Implementation

  • 5.1 Overview of the Proposed Algorithm for Image Smoothing
  • 5.2 Steps for Adaptive Window Selection and Smoothing
  • 5.3 Incorporating Edge-Preservation into the Smoothing Process
  • 5.4 Computational Efficiency and Real-Time Implementation
  • 5.5 Challenges in Handling Diverse Image Content

• Experimental Setup and Evaluation

  • 6.1 Datasets Used for Testing the Smoothing Algorithm
  • 6.2 Evaluation Metrics: Quality, Preservation of Structures, and Smoothing Efficiency
  • 6.3 Comparative Methods for Smoothing and Structure Preservation
  • 6.4 Cross-Validation and Experimental Protocols
  • 6.5 Example Images and Ground Truth for Evaluation

• Results and Discussion

  • 7.1 Visual Examples of Image Smoothing and Structure Preservation
  • 7.2 Quantitative Evaluation of Smoothing Performance
  • 7.3 Impact of the Adaptive Window Size on Different Image Types
  • 7.4 Comparison with Traditional and Advanced Smoothing Methods
  • 7.5 Discussion of Edge Preservation and Fine Detail Retention

• Applications of the Adaptive Window Approach

  • 8.1 Noise Reduction in Medical Imaging
  • 8.2 Image Denoising for Photography and Computer Vision
  • 8.3 Real-Time Smoothing for Video and Motion Analysis
  • 8.4 Texture and Edge Preservation in Image Compression
  • 8.5 Applications in 3D Reconstruction and Surface Processing

• Challenges and Future Directions

  • 9.1 Handling Complex and Noisy Data
  • 9.2 Real-Time Processing for Large-Scale Images and Videos
  • 9.3 Generalization to Different Types of Image Structures
  • 9.4 Integration with Deep Learning and Neural Networks for Smoothing
  • 9.5 Future Research on Adaptive Filtering and Structure-Preserving Techniques

• Conclusion

  • 10.1 Summary of Key Contributions and Findings
  • 10.2 Practical Implications of the Adaptive Window Approach
  • 10.3 Limitations of the Current Approach
  • 10.4 Future Work and Potential Improvements

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