Computational Photography

Methods and Applications

Rastislav Lukac

• Publisher: CRC Press
• Year: 2017
• Print ISBN: 9780367837860
• eBook ISBN: 9781351833974
• Topic: Computer Science
• Subtopic: Computer Graphics
• Index: Computer Science

Computational photography refers broadly to imaging techniques that enhance or extend the capabilities of digital photography. This new and rapidly developing research field has evolved from computer vision, image processing, computer graphics and applied optics—and numerous commercial products capitalizing on its principles have already appeared in diverse market applications, due to the gradual migration of computational algorithms from computers to imaging devices and software.

Computational Photography: Methods and Applications provides a strong, fundamental understanding of theory and methods, and a foundation upon which to build solutions for many of today's most interesting and challenging computational imaging problems. Elucidating cutting-edge advances and applications in digital imaging, camera image processing, and computational photography, with a focus on related research challenges, this book:

• Describes single capture image fusion technology for consumer digital cameras
• Discusses the steps in a camera image processing pipeline, such as visual data compression, color correction and enhancement, denoising, demosaicking, super-resolution reconstruction, deblurring, and high dynamic range imaging
• Covers shadow detection for surveillance applications, camera-driven document rectification, bilateral filtering and its applications, and painterly rendering of digital images
• Presents machine-learning methods for automatic image colorization and digital face beautification
• Explores light field acquisition and processing, space-time light field rendering, and dynamic view synthesis with an array of cameras

Because of the urgent challenges associated with emerging digital camera applications, image processing methods for computational photography are of paramount importance to research and development in the imaging community. Presenting the work of leading experts, and edited by a renowned authority in digital color imaging and camera image processing, this book considers the rapid developments in this area and addresses very particular research and application problems. It is ideal as a stand-alone professional reference for design and implementation of digital image and video processing tasks, and it can also be used to support graduate courses in computer vision, digital imaging, visual data processing, and computer graphics, among others.

1

Single Capture Image Fusion

James E. Adams, Jr., John F. Hamilton, Jr., Mrityunjay Kumar, Efraín O. Morales, Russell Palum, and Bruce H. Pillman

1.1 Introduction

1.1.1 Image Fusion

1.1.2 Chapter Overview

1.2 Color Camera Design

1.2.1 Three-Channel Arrays

1.2.2 Four-Channel Arrays

1.2.3 Color Fidelity versus Spatial Resolution

1.3 Demosaicking

1.3.1 Special Functions and Transforms

1.3.2 The Panchromatic Sensor

1.3.3 Demosaicking Algorithm Overview

1.3.3.1 Rectilinear Grid-Based Nonadaptive Interpolation

1.3.3.2 Diamond Grid-Based Nonadaptive Interpolation

1.3.4 The Bayer Color Filter Array

1.3.4.1 Bilinear Interpolation

1.3.4.2 Adaptive Interpolation

1.3.5 Four-Channel CFA Demosaicking

1.3.5.1 Adaptive Linear Interpolation

1.3.5.2 Adaptive Cubic Interpolation

1.3.5.3 Alternating Panchromatic

1.3.5.4 Double Panchromatic

1.3.5.5 Triple Panchromatic

1.3.5.6 Two-Color Alternating Panchromatic

1.3.6 Comments

1.4 Noise and Noise Reduction

1.4.1 Image Noise Propagation

1.4.2 Image Noise Reduction

1.4.2.1 High-Frequency Noise Reduction

1.4.2.2 Mid-Frequency and Low-Frequency Noise Reduction

1.4.3 Image Sharpening

1.5 Example Single-Sensor Image Fusion Capture System

1.6 Conclusion

Acknowledgment

Appendix

References

1.1 Introduction

A persistent challenge in the design and manufacture of digital cameras is how to improve the signal-to-noise performance of these devices while simultaneously maintaining high color fidelity captures. The present industry standard three-color channel system is constrained in that the fewest possible color channels are employed for the purposes of both luminance and chrominance image information detection. Without additional degrees of freedom, for instance, additional color channels, digital camera designs are generally limited to solutions based on improving sensor hardware (larger pixels, lower readout noise, etc.) or better image processing (improved denoising, system-wide image processing chain optimization, etc.) Due to being constrained to three channels, the requirements for improved signal-to-noise and high color fidelity are frequently in opposition to each other, thereby providing a limiting constraint on how much either can be improved. For example, to improve the light sensitivity of the sensor system, one might wish to make the color channels broader spectrally. While this results in lower image noise in the raw capture, the color correction required to restore the color fidelity amplifies the noise so much that there can be a net loss in overall signal...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Epigraph
• Dedication
• Table of Contents
• Preface
• The Editors
• Contributors
• Chapter 1 Single Capture Image Fusion
• Chapter 2 Single Capture Image Fusion with Motion Consideration
• Chapter 3 Lossless Compression of Bayer Color Filter Array Images
• Chapter 4 Color Restoration and Enhancement in the Compressed Domain
• Chapter 5 Principal Component Analysis-Based Denoising of Color Filter Array Images
• Chapter 6 Regularization-Based Color Image Demosaicking
• Chapter 7 Super-Resolution Imaging
• Chapter 8 Image Deblurring Using Multi-Exposed Images
• Chapter 9 Color High-Dynamic Range Imaging: Algorithms for Acquisition and Display
• Chapter 10 High-Dynamic Range Imaging for Dynamic Scenes
• Chapter 11 Shadow Detection in Digital Images and Videos
• Chapter 12 Document Image Rectification Using Single-View or Two-View Camera Input
• Chapter 13 Bilateral Filter: Theory and Applications
• Chapter 14 Painterly Rendering
• Chapter 15 Machine Learning Methods for Automatic Image Colorization
• Chapter 16 Machine Learning for Digital Face Beautification
• Chapter 17 High-Quality Light Field Acquisition and Processing
• Chapter 18 Dynamic View Synthesis with an Array of Cameras
• Index