Nonlinear Inverse Problems in Imaging: A Comprehensive Guide

Key Features:

• Thorough coverage of fundamental concepts and advanced techniques in nonlinear inverse problems.
• Real-world applications in various imaging domains, including medical imaging, computer vision, and geophysical imaging.
• Detailed derivations and explanations of core algorithms and methods, making the content accessible to readers of all levels.
• Comprehensive exploration of regularization techniques and their impact on image reconstruction.
• In-depth discussion of image quality assessment metrics and optimization strategies.
• Accompanying MATLAB code and datasets for hands-on implementation and experimentation.

Applications in the Real World:

Our book demonstrates the transformative impact of nonlinear inverse problems in various real-world applications. Here are a few examples:

• Medical Imaging:
  • Reconstructing high-quality images from noisy and incomplete medical data.
  • Quantifying tissue properties and diagnosing diseases using advanced imaging techniques.
• Computer Vision:
  • Depth estimation and 3D reconstruction from single or multiple images.
  • Object tracking and segmentation in complex environments.
• Geophysical Imaging:
  • Seismic data processing and subsurface imaging for oil and gas exploration.
  • Non-destructive testing and characterization of materials.

About the Authors:

The book is authored by a team of renowned experts in the field of nonlinear inverse problems. These experts have decades of collective experience in research, development, and teaching. Their insights and guidance provide a unique and authoritative perspective on the subject matter.

• Dr. John Doe: Professor of Electrical Engineering at Stanford University.
• Dr. Jane Smith: Senior Research Scientist at Google AI.
• Dr. David Brown: Chief Scientist at XYZ Imaging Technologies.

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