High Dynamic Range Imaging: Sensors and Architectures

✍ Scribed by Arnaud Darmont

• Publisher: SPIE Press
• Year: 2019
• Tongue: English
• Leaves: 181
• Series: Press Monographs
• Edition: 2
• Category: Library

✦ Synopsis

Illumination is a crucial element in many applications, matching the luminance of the scene with the operational range of a camera. When luminance cannot be adequately controlled, a high dynamic range (HDR) imaging system may be necessary. These systems are being increasingly used in automotive on-board systems, road traffic monitoring, and other industrial, security, and military applications. This book provides readers with an intermediate discussion of HDR image sensors and techniques for industrial and non-industrial applications. It describes various sensor and pixel architectures capable of achieving HDR imaging, as well as software approaches to make high dynamic range images out of lower dynamic range sensors or image sets. Some methods for automatic control of exposure and dynamic range of image sensors are also introduced. This edition introduces CMOS pixel and image sensor design concepts and circuits.

✦ Table of Contents

Copyright
Acknowledgments
Preface
Chapter 1 Introduction
1.1 Applications Requiring a Higher Dynamic Range
1.2 High Dynamic Range Photography
1.3 Scientific Applications
1.4 High Dynamic Range, Wide Dynamic Range, and Extended Dynamic Range
1.5 Reducing the Exposure Time
1.6 HDR Applications That Do Not Require HDR Images
1.7 Image Histograms
1.8 Outline and Goals
1.9 Defining a Camera
Chapter 2 Dynamic Range
2.1 Image Sensor Theory
2.1.1 Light source, scene, pixel, and irradiance
2.1.2 Sensing node and light–matter interaction
2.1.3 Pixel
2.1.4 Pixel array
2.1.5 Readout circuits
2.1.6 Image encoding
2.2 Low-Light Imaging Limitations
2.2.1 Noise sources summary
2.2.2 Lowest detectable limit
2.3 Bright-Light Imaging Limitations
2.3.1 Saturation
2.3.2 Highest detectable level
2.4 Signal-to-Noise Ratio
2.5 Dynamic Range Gaps
2.5.1 Response curve
2.5.2 Dynamic range gaps
2.5.3 Presence function of dynamic range gaps
2.6 Dynamic Range
2.6.1 Definition
2.6.2 Remark
2.6.3 Relative measurement
2.7 Image Information
2.8 Image Information of a Real Scene
2.9 Human Vision System and Its Dynamic Range
2.9.1 General properties of human vision
2.9.2 Dynamic range of the human eye
2.9.3 Noise perception
Chapter 3 Hardware Methods to Extend the Dynamic Range
3.1 Introduction: Integrating Linear Pixels
3.1.1 Rolling-shutter pixel architecture
3.1.2 Global-shutter-pixel architecture
3.1.3 SNR and dynamic range study
3.2 Multilinear Pixels
3.2.1 Principle
3.2.2 How can multiple segments be realized practically?
3.2.3 Equations of the 3T pixel: reset and multiple-segment HDR exposure
3.2.4 Equations of the 3T pixel: readout
3.2.5 Equations of the 3T pixel: power supply rejection ratio
3.2.6 Charge injection and clock feedthrough
3.2.7 Multiple-segment method based on well sizing
3.2.8 Dynamic compression
3.2.9 SNR and dynamic range study
3.3 Multiple Sampling
3.4 Multiple-Sensing Nodes
3.5 Logarithmic Pixels
3.6 Logarithmic Photovoltaic Pixel
3.7 Time to Saturation
3.8 Gradient-Based Image
3.9 Light to Frequency
3.10 Multiple Readout Gains
3.11 Other Methods
3.12 Multiple-Exposure Windows
3.13 Combined Methods within One Sensor
3.14 Summary
3.15 Companding ADCs
3.16 Extended-Dynamic-Range Color Imaging
3.17 LED Flicker Mitigation
3.18 Sensors Used in Applications
3.19 3D Stacking
3.20 Packaging Issues
Chapter 4 Software Methods to Extend the Dynamic Range
4.1 General Structure of a Software Approach
4.2 High Dynamic Range Image Data Merging
4.2.1 Ideal case
4.2.2 Real sensors and cameras
4.2.3 Debevec’s algorithm
4.2.4 Alternate method: Mann and Picard
4.2.5 Alternate method: Mitsunaga and Nayar
4.2.6 Alternate method: Robertson et al.
4.3 Noise Removal
4.3.1 Temporal pixel noise
4.3.2 Ghosts and misalignments
4.4 Tone Mapping
4.5 Software Methods Applicable to Certain Image Processing Applications
4.6 Sensors with Integrated Processing
4.7 Simulated High Dynamic Range Images
Chapter 5 Optical Limitations
5.1 Lens Glare
5.2 Modulation Transfer Function
5.3 Conclusions
Chapter 6 Automatic High Dynamic Range Control
6.1 Automatic Exposure of Linear Sensors
6.1.1 Principle
6.1.2 Brightness calculation
6.1.3 Filtering and stability for machine vision
6.1.4 Filtering and stability for display
6.1.5 Guard-band-based filtering
6.2 Automatic Exposure of High Dynamic Range Sensors
6.3 Offset Control
Chapter 7 High Dynamic Range File Formats
7.1 Color Space
7.1.1 Introduction
7.1.2 Color space definition
7.2 Storing Image Data of Extended Dynamic Range Cameras
7.3 Storing Data of Radiance Maps and High Dynamic Range Software: Direct Pixel Encoding Methods
7.3.1 IEEE single precision floating point
7.3.2 Pixar™ log encoding
7.3.3 Radiance RGBE
7.3.4 SGI™ LogLuv TIFF
7.3.5 Industrial Light and Magic™ OpenEXR
7.3.6 Unified Color™ BEF
7.3.7 Microsoft/HP™ scRGB
7.3.8 JPEG XR
7.3.4 SGI™ LogLuv TIFF
7.3.5 Industrial Light and Magic™ OpenEXR
7.3.6 Unified Color™ BEF
7.3.7 Microsoft/HP™ scRGB
7.3.8 JPEG XR
7.3.9 Summary of file formats
7.3.10 Gencam
7.4 Storing Data of Radiance Maps and High Dynamic Range Software: Gradient-Based and Flow-Based Methods
Chapter 8 Testing High Dynamic Range Sensors, Cameras, and Systems
8.1 Testing of Software-Based Systems
8.2 Testing of Non-High Dynamic Range (Linear) Sensors and Cameras
8.2.1 The ISO approach
8.2.2 The EMVA1288 approach
8.3 Testing of High Dynamic Range Sensors and High Dynamic Range Sensor-Based Cameras
8.3.1 The ISO approach
8.3.2 The EMVA1288 approach
8.3.3 Two-projector approach
8.3.4 Projector-and-display approach
8.4 Contrast Detection Probability
Chapter 9 Dynamic Range in Non-Visible and 3D Imaging Devices
9.1 Infrared Imaging
9.2 3D Imaging
Chapter 10 Conclusions
10.1 Important Figures of Merit of a High Dynamic Range Sensor
10.2 Questions
Bibliography
Author Biography