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E-grāmata: Single-Sensor Imaging: Methods and Applications for Digital Cameras

Edited by (Foveon, Inc. / Sigma Corp., San Jose, California, USA)
  • Formāts: 600 pages
  • Sērija : Image Processing Series
  • Izdošanas datums: 03-Oct-2018
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781420054538
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  • Bibliotēkām
  • Formāts: 600 pages
  • Sērija : Image Processing Series
  • Izdošanas datums: 03-Oct-2018
  • Izdevniecība: CRC Press Inc
  • Valoda: eng
  • ISBN-13: 9781420054538
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A Decade of Extraordinary Growth

The past decade has brought a surge of growth in the technologies for digital color imaging, multidimensional signal processing, and visual scene analysis. These advances have been crucial to developing new camera-driven applications and commercial products in digital photography. Single-Sensor Imaging: Methods and Applications for Digital Cameras embraces this extraordinary progress, comprehensively covering state-of-the-art systems, processing techniques, and emerging applications.

Experts Address Challenges and Trends

Single-Sensor Imaging: Methods and Applications for Digital Cameras presents leading experts elucidating their own accomplishments in developing the technologies reshaping this field. The editor invited renowned authorities to address specific research challenges and recent trends in their particular areas of expertise. The book discusses single-sensor digital color imaging fundamentals, including reusable embedded software platform, digital camera image processing chain, optical filter and color filter array designs. It also details the latest techniques and approaches in contemporary and traditional digital camera color image processing and analysis for various sophisticated applications, including:





Demosaicking and color restoration White balancing and color transfer Color and exposure correction Image denoising and color enhancement Image compression and storage formats Red-eye detection and removal Image resizing Video-demosaicking and superresolution imaging Image and video stabilization

A Solid Foundation of Knowledge to Solve Problems

Single-Sensor Imaging: Methods and Applications for Digital Cameras builds a strong fundamental understanding of theory and methods for solving many of todays most interesting and challenging problems in digital color image and video acquisition, analysis, processing, and storage. A broad survey of the existing solutions and relevant literature makes this book a valuable resource both for researchers and those applying rapidly evolving digital camera technologies.
Single-Sensor Digital Color Imaging Fundamentals
1(30)
Rastislav Lukac
Introduction
1(1)
Color Image Acquisition in Digital Cameras
2(5)
Three-Sensor Digital Cameras
3(1)
Single-Sensor Digital Cameras
4(3)
From Raw sensor Data to Digital Photographs
7(5)
Pipelining Image Processing Solutions
7(2)
Design Alternatives
9(3)
Visual Artifacts in Digital Camera Images
12(5)
Image Noise
12(1)
Demosaicking Artifacts
13(1)
Coloration Shifts
14(1)
Exposure Shifts
15(1)
Image Compression Artifacts
16(1)
What Is Really Important in Digital Camera Image Processing?
17(7)
Spatial Characteristics
17(2)
Structural Characteristics
19(2)
Spectral Characteristics
21(3)
Temporal Characteristics
24(1)
Conclusion
24(1)
References
25(6)
Reusable Embedded Software Platform for Versatile Single-Sensor Digital Cameras
31(36)
Wen-Chung Kao
Hung-Hsin Wu
Sheng-Yuan Lin
Introduction
32(1)
Hardware Platform Overview
33(8)
Zoom Lens Module and Auto Focus Control
34(1)
Overview of Zoom Lens Control
34(1)
Design Considerations of Auto Focus Algorithms
35(1)
Image Sensor and Auto Exposure
36(1)
Overview of Image Sensors
36(2)
Design Considerations of Auto Exposure Algorithms
38(1)
Camera Signal Processor
39(2)
Embedded Software Platform
41(4)
Software Programming Layers
41(2)
Software Design Reuse
43(1)
Application Program Interface
43(1)
Device Driver Interface
44(1)
Software Design Methodology
45(4)
Task Scheduling by Real-Time Operating System
45(2)
DSP Subsystem Management
47(1)
Hardware Accelerator Management
47(1)
Dynamic Memory Management
48(1)
Software Module Design Guidelines
49(9)
Available Hardware Resources Analysis
49(1)
Previewing Images on Color LCD
49(1)
MPEG Audio/Video Playback
50(1)
Job Scheduling and Resource Allocation
51(3)
Background processing and Data Buffering
54(1)
Continuous Still Image Capture
55(1)
MPEG Audio/Video Recording
55(2)
Power Aware Design
57(1)
Embedded Software design of Built-in Automatic Camera Calibration
58(5)
Automatic Camera Calibration Flow
58(2)
Mechanical Shutter delay Calibration
60(2)
Image Sensor Calibration
62(1)
Conclusion
63(1)
Acknowledgment
64(1)
References
64(3)
Digital Camera Image Processing chain design
67(38)
James E. Adams, Jr.
John F. Hamilton, Jr.
Introduction
68(1)
A First Image Processing Path and the Basic Building Blocks
69(13)
Cost and User Sensitivity Considerations
69(1)
Systematic Sensor Data Correction
70(1)
Dark Floor Subtraction
70(2)
Structured Noise Reduction
72(1)
CFA Data Correction
72(1)
Stochastic Noise Reduction
72(1)
Exposure and White Balance Correction
73(1)
Adjusted CFA Image and Image Data Calibration
74(1)
Color filter Array Interpolation
75(1)
Stochastic Color Noise Reduction
76(1)
Color Correction
77(1)
Image Space Rendering
78(1)
Tone Scale and Gamma Correction
78(2)
Edge Enhancement
80(2)
Variations on the First Image Processing Path
82(16)
Luminance-Chrominance Processing
83(1)
Spatial Frequency Processing
83(2)
Computing Environments
85(1)
Intermediate Data Storage
85(4)
Physical Environments
89(1)
Resizing and Compression
90(1)
Image Resizing
90(2)
Image Compression
92(1)
Other Factors
93(1)
Bit Depth
94(1)
Nonlinear Photometric Spaces
95(1)
Extended Dynamic Range
96(2)
How video Differs from Still Photography
98(2)
Conclusion
100(1)
References
101(4)
Optical Antialiasing Filters
105(32)
Russ Palum
Introduction
105(4)
Aliasing
106(3)
Digital Photographic Systems
109(1)
Nyquist Domain Graph
109(2)
The Four-Spot Birefringent Antialiasing Filter
111(4)
Modulation Transfer Function
115(2)
Fourier Analysis and Convolution
117(1)
Fourier Transform Pairs
118(2)
Image System Response
120(4)
System Modulation Transfer Function
122(2)
Reconstruction
124(3)
Construction
127(3)
Testing
130(3)
Conclusions
133(1)
Acknowledgments
134(1)
References
134(3)
Spatio-Spectral Sampling and Color Filter Array Design
137(16)
Keigo Hirakawa
Patrick J. Wolfe
Introduction
137(2)
Spatio-Spectral Analysis of Existing Patterns
139(4)
Color Filter Arrays
139(3)
Aliased Sensor Data and Demosaicking
142(1)
Spatio-Spectral Color Filter Array Design
143(3)
Frequency-Domain Specification of Color Filter Array Designs
143(2)
Analysis and Design Trade-Offs
145(1)
Linear Demosaicking via Demodulation
146(1)
Examples and Analysis
147(3)
Conclusion
150(1)
References
150(3)
Mosaicking and Demosaicking in the Design of Multispectral Digital Cameras
153(30)
Lidan Miao
Hairong Qi
Wesley E. Snyder
Introduction
153(2)
Mosaicked Filter Array Patterns and Their Design Philosophy
155(5)
Color Filter Arrays
156(1)
Biological Relevance
157(1)
Design Requirements for Multispectral Filter Arrays
158(2)
A Generic Filter Array Design Method
160(1)
A Generic Binary Tree-based Demosaicking Method
161(6)
Correlation Analysis of Multispectral Images
163(1)
Band selection
164(1)
Pixel Selection
164(2)
Interpolation
166(1)
Experiments and Results
167(10)
Pure Evaluation of Generated Filter Arrays
167(1)
Static Coefficient
168(1)
Consistency Coefficient
168(4)
Evaluation of Mosaicked Multispectral Imaging System
172(1)
Effectiveness of Binary Tree an Edge Sensing Method
173(3)
Comparison with Advanced CFA Demosaicking algorithms
176(1)
Conclusions
177(1)
Acknowledgment
177(1)
References
178(5)
Color Filter Array Sampling of Color Images: Frequency-Domain analysis and Associated Demosaicking Algorithms
183(30)
Eric Dubois
Introduction
183(1)
Geometric Structure of the Color-Filter Array
184(2)
Formation and Representation of the CFA Image
186(12)
Formation of the CFA Image
186(1)
Frequency-Domain Representation of the CFA Image
186(5)
Examples
191(1)
Hexagonal Pattern
191(2)
Diagonal Stripe Pattern
193(3)
Four-Color Pattern
196(1)
Summary
197(1)
Demosaicking Based on the Frequency-Domain Representation
198(5)
The Demosaicking Problem
198(1)
Algorithms Derived from the Frequency-Domain Representation
199(4)
Filter Design for CRA signal Demultiplexing
203(6)
Concluding Remarks
209(1)
Acknowledgements
209(1)
Appendix: Lattices and Two-Dimensional Signals on Lattices
209(2)
References
211(2)
Linear Minimum Mean Square Error Demosaicking
213(26)
David Alleysson
Brice Chaix de Lavarene
Sabine Susstrunk
Jeanny Herault
Introduction
213(4)
Trichromacy in Human Vision
214(1)
Digital Color Image Encoding
215(1)
Image Acquisition through Single Chip Digital Camera
216(1)
Color Filter Array Signal Representation
217(6)
Luminance-Chrominance Representation of Color Images
217(2)
Luminance-Chrominance in Color Filter Arrays
219(2)
Examples of Practical CFAs
221(2)
Linear Systems for Luminance-Chrominance Estimation
223(10)
Linear Estimation Using Constant Ratio Hypothesis
224(2)
Filter Design from the CFA Spectrum
226(1)
Wiener Estimation
227(1)
Direct RGB Estimation
228(1)
Estimation through Luminance and Chrominance
229(1)
Performance Comparison of Different CFAs
230(3)
Nonlinear and Adaptive Methods
233(2)
Accurate Luminance Method
234(1)
Frequency Domain Method
234(1)
Conclusion
235(1)
References
235(4)
Color Filter Array Image Analysis for Joint Demosaicking and Denoising
239(28)
Keigo Hirakawa
Introduction
239(5)
A Comment About Model Assumptions
242(1)
Terminologies and Notational Conventions
242(2)
Noise Model
244(2)
Spectral Analysis of CFA Image
246(3)
Wavelet Analysis of CFA Image
249(5)
Constrained Filtering
254(3)
Missing Data
257(2)
Filterbank Coefficient Estimation
259(2)
Conclusion
261(1)
Acknowledments
261(3)
References
264(3)
Automatic White Balancing in Digital Photography
267(28)
Edmund Y. Lam
George S.K. Fung
Introduction
267(1)
Human Visual System and Color Theory
268(10)
Illumination
269(1)
Object
270(1)
Color Stimulus
271(2)
Human Visual System
273(2)
Color Matching
275(3)
Challenges in Automatic White Balancing
278(1)
Automatic White Balancing Algorithms
279(9)
Gray World
280(1)
White Patch
281(1)
Iterative White Balancing
282(2)
Illuminant Voting
284(2)
Color by Correlation
286(1)
Other Methods
287(1)
Implementations and Quality Evaluations
288(4)
Conclusion
292(1)
Acknowledgments
292(1)
References
292(3)
Enhancement of Digital Photographs Using Color Transfer Techniques
295(28)
Francois Pitie
Anil Kokaram
Rozenn Dahyot
Introduction
295(4)
How Does Color Grading Work?
296(2)
How to Deal with Content Variations
298(1)
Color Distribution Transfer
299(2)
Linear Color Distribution Transfer Techniques
301(3)
Independent Transfer
302(1)
Cholesky Decomposition
302(1)
Principal Axes Transfer
302(1)
Linear Monge-Kantorovitch Solution
303(1)
Nonlinear Color Distribution Transfer Techniques
304(5)
Independent Transfer
304(1)
Composition Transfer
304(1)
The Discrete Kantorovitch Solution
305(1)
Transfer via the Radon Transform
306(3)
What Color Space to Choose?
309(3)
Reducing Grain Noise Artifacts
312(5)
Reducing the Stretching by Adjusting the Distributions
312(1)
Reducing the Artifacts by Adjusting the Gradient Fields
313(4)
Application Results
317(1)
Parting Remarks
317(2)
Acknowledgments
319(1)
References
319(4)
Exposure Correction for Imaging Devices: An Overview
323(28)
Sebastiano Battiato
Giusepope Messina
Alfio Castorina
Introduction
323(1)
Exposure Metering Techniques
324(5)
Classical Approaches
325(1)
Spot Metering
325(1)
Partial Area Metering
326(1)
Center-Weighted Average Metering
327(1)
Average Metering
327(1)
Advanced Approaches
327(1)
Matrix or Multi-Zone Metering
327(2)
Exposure Correction Content Dependent
329(6)
Feature Extraction: Contrast and Focus
330(1)
Feature Extraction: Skin Detection
331(2)
Exposure Correction
333(1)
Exposure Correction Results
334(1)
Bracketing and Advanced Applications
335(13)
The Sensor Versus the World
336(1)
Camera Response Function
337(3)
High Dynamic Range Image Construction
340(1)
The Scene Versus the Display Medium
341(1)
Histogram Adjustment
342(1)
Chiu's Local Operator
343(1)
Bilateral Filtering
344(1)
Photographic Tone Reproduction
345(1)
Gradient Compression
346(2)
Conclusion
348(1)
References
348(3)
Digital Camera Image Storage Formats
351(30)
Kenneth A. Parulski
Robert Reisch
Introduction
351(1)
Image Formats, Memory Formats, and Metadata
352(2)
History of Image Formats for Digital Cameras
354(3)
Exif-JPEG Image Format Structure
357(5)
Exif-JPEG Digital Camera Metadata
362(7)
Raw Image Formats
369(2)
Directory and Control Formats
371(4)
Advanced Image Formats
375(1)
Conclusion
376(1)
References
377(4)
Modelling of Image Processing Pipelines in Single-Sensor Digital Cameras
381(24)
Nai-Xiang Lian
Vitali Zagorodnov
Yap-Peng Tan
Introduction
381(1)
Elements of Digital Still Camera Image Processing Pipeline
382(2)
Demosaicking
382(1)
Color Adjustments
383(1)
Compression
383(1)
Alternative Image Processing Pipelines
384(1)
Performance Modelling
385(5)
Modelling Pipeline Elements
385(2)
Modelling Interactions
387(2)
Modelling via Taylor Series Expansion
389(1)
Modelling of Individual Digital Still Camera Processing Elements
390(4)
Modelling of Demosaicking Errors
390(1)
Modelling of Compression Errors
391(1)
Full-Color Image Compression
392(1)
CFA Image Compression
392(1)
Evaluation of the Compression Error Models
393(1)
Modelling of Interactions
394(3)
Color Adjustments and Demosaicking / Compression
394(1)
Demosaicking and Compression
395(2)
Performance Evaluation of Digital Still Camera Processing Pipelines
397(3)
Summary
400(2)
References
402(3)
Lossless Compression of Color Mosaic Images and Videos
405(24)
Ning Zhang
Xiaolin Wu
Lei Zhang
Introduction
405(1)
Compression and Demosaicking in the Camera Imaging Pipeline
406(2)
Deinterleaving
408(3)
Interchannel Coding
411(1)
Towards Direct Compression
412(2)
Wavelet Compression
414(6)
Interframe Lossless Video Compression
420(6)
Switch of Coding Mode
421(2)
Interframe Coding
423(2)
Experimental Results
425(1)
Conclusion
426(1)
Acknowledgment
427(1)
References
427(2)
Automatic Red-Eye Removal for Digital photography
429(30)
Francesca Gasparini
Raimondo Schettini
Introduction
429(4)
Red-Eye Detection
433(7)
Red-Eye Detection Within a Confined Region
433(5)
Red-Eye Detection Within the Whole Image
438(2)
Methods for the Reduction of the Red-Eye Search Space
440(3)
Skin and Sclera Detection
440(2)
Face Detection
442(1)
Eye Detection
443(1)
Red-Eye Correction
443(6)
A Complete Procedure for Automatic Red-Eye Removal
449(4)
Image Color Correction
450(1)
Face Detection
450(2)
Red-Eye Detection
452(1)
Red-Eye Removal
452(1)
Evaluation and Conclusion
453(2)
References
455(4)
Image Resizing Solutions for Single-Sensor Digitl Cameras
459(26)
Rastislav Lukac
Introduction
460(1)
Single-Sensor Image Resizing Framewoks
461(1)
Demosaicked Image Resizing
462(12)
One-Dimensional Transforms
463(1)
Tow-Dimensional Standard Transorms
463(1)
Pixel Omission
464(1)
Pixel Replication
464(1)
Nearest Neighbor Interpolation
464(1)
Median Interpolation
465(1)
Bilinear / Bicubic Interpolation
465(3)
Fast Kernel-Based Solutions for Image Upsampling
468(1)
Spatial Interpolation
469(1)
Joint Spatial Interpolation and Edge Enhaancement
469(1)
Edge-Adaptive Resizing
470(1)
Vector Processing Solutions
471(1)
Pixel-Selecetive Framework
472(1)
Data-Adaptive Framework
473(1)
Image Resizing in the Compressed Domain
474(1)
Color Filter Array Image Resizing
474(5)
Structure Conversion-Based Approaches
474(2)
Pixel Mapping-Based Approaches
476(2)
Interpolation Using Spatial Correlation
478(1)
Interpolation Using Spatialand Spectral Correlations
478(1)
Joint Image REsizing and Demonsaicking
479(3)
Fully Integrated Demosaicking and Resizing
481(1)
Conclusion
482(1)
Refereces
482(3)
Video-Demosaicking
485(18)
Lei Zhang
Wei Lian
Simultaneous Demosaicking and Resolution Enhancement from Under-Sampled Image Sequences
503(32)
Sina Farsiu
Dirk Robinson
Michael Elad
Peyman Mialnfar
Introduction
503(3)
Sequential Demosaicking and Superresolution
506(9)
Single-Frame Demosaicking
506(4)
Multiframe Superresolution
510(5)
Sequential Processing Example
515(1)
Multiframe Demosaicking
515(4)
Modelling the Problem
515(3)
Multiframe Demosaicking Examples
518(1)
Fast and Dynamic Multifram Demosaicking
519(6)
Fast Multiframe Demosaicking
519(1)
Dat fusion Step
520(1)
Deblurring and Interpolation Step
521(1)
Dynamic Multiframe Demosaicking
521(3)
Example of Dynamic Multiframe Demosaicking
524(1)
Conclusion
525(1)
Acknowledgments
525(1)
Appendix: Motion Estimation
526(2)
References
528(7)
An Overview of Image/Video Stabilization Techniques
535(28)
Wen-Chung Kao
Sheng-Yan Lin
Introduction
535(2)
Optical Image Stabilization
537(6)
Popular Solutions
537(1)
Flexible Prism Architecture
538(1)
In-Lens and In-Camera Optical Image Stabilization
539(2)
Control Mechanisms
541(2)
Digital and Electronic Image Stabilization
543(4)
Problem Formulation
544(2)
System Architecture
546(1)
Global Motion Estimation of Digital Image Stabilization
547(6)
Local Motion Estimation
548(1)
Sum-of-Absolute-Differences
548(1)
Representative Points
549(1)
Gray-Coded Bit-Plane Matching
550(1)
Phase Correlations
550(1)
Irregular Local Motion Vector Detection
551(1)
Deviation Analysis of Sum-of-Absolute-Differences
551(1)
Low-Pass Filtering
551(1)
Histogram Analysis
551(1)
Global Motion Vector Determination
552(1)
Motion Compensation
553(4)
Compensation Motion Vector Determination
554(1)
Moving Average
554(1)
Kalman Filter
555(1)
Image Warping
556(1)
Conclusion
557(1)
Acknowledgment
558(1)
References
558(5)
Index 563
Epson Edge, Epson Canada Ltd., Ontario, CANADA The Pennsylvania State University, Malvern, USA