视频图像处理是一门交叉学科，涉及图像处理、模式识别、计算机视觉等。随着其发展和应用的深入，视频图像处理已成为工程学、计算机科学、信息科学等的重要组成部分。本书系统阐述视频图像处理的概念、原理和最新方法，主要内容包括：视频图像处理基础、视频图像复原、显著性检测、目标检测、目标跟踪、目标识别、红外视频图像处理、水下视频图像处理等。本书以其独特的专业内容、精练的语言形式，适用于电子信息类、计算机类等相关专业本科生和研究生专业课程双语教学（全英文授课）的使用教材。

Preface
Chapter 1 Introduction
1.1 Video Image Restoration
1.2 Target Detection in Video Images
1.3 Target Tracking in Surveillance Videos
1.4 Video Image Saliency Detection
1.5 Underwater Video Image Processing
1.6 Vision Image Processing for River Surface Velocimetry
1.7 Remote Sensing Imagery Processing and Analysis
References
Chapter 2 Video Image Restoration
2.1 Automatic Image De-weathering Using Physical Model and Maximum Entropy
2.1.1 Introduction
2.1.2 Automatic Contrast Restoration
2.1.3 Experimental Results
2.1.4 Conclusion
2.2 Image Dehazing Using Degradation Model and Group-based Sparse Representation
2.2.1 Introduction
2.2.2 Preliminaries
2.2.3 Presented Approach
2.2.4 Experimental Results
2.2.5 Conclusion
References
Chapter 3 Target Detection in Video Images
3.1 A Sparse Representation-based Method for Infrared Dim Target Detection under Sea-sky Background
3.1.1 Introduction
3.1.2 Related Work
3.1.3 Proposed Approach
3.1.4 Experimental Results
3.1.5 Conclusion
3.2 Spatiotemporal Saliency Model for Small Moving Object Detection in Infrared Videos
3.2.1 Introduction
3.2.2 Spatiotemporal Saliency Model for Infrared Videos
3.2.3 Experimental Results
3.2.4 Conclusion
References
Chapter 4 Target Tracking in Surveillance Videos
4.1 Multi-feature Local Sparse Representation for Infrared Pedestrian Tracking
4.1.1 Introduction
4.1.2 Related Work
4.1.3 Proposed Method
4.1.4 Experimental Results
4.1.5 Conclusion
4.2 Spatiotemporal Difference-of-Gaussians Filters for Robust Infrared Small Target Tracking in Various Complex Scenes
4.2.1 Introduction
4.2.2 Spatiotemporal Gabor Filters
4.2.3 Combined Difference-of-Gaussians Filter for Small IR Target Detection
4.2.4 Spatiotemporal Difference-of-Gaussians Filters for Small IR Target Tracking
4.2.5 Experimental Results
4.2.6 Conclusions
References
Chapter 5 Video Image Saliency Detection
5.1 A Novel Visual Saliency Detection Method for Infrared Video Sequences
5.1.1 Introduction
5.1.2 Proposed Method
5.1.3 Experimental Results
5.1.4 Conclusion
5.2 Visual Saliency Detection Based on In-depth Analysis of Sparse Representation
5.2.1 Introduction
5.2.2 Related Work
5.2.3 Proposed Method
5.2.4 Experimental Results
5.2.5 Conclusion
References
Chapter 6 Underwater Video Image Processing
6.1 An Integrative Framework for Effective Restoration of Underwater Images
6.1.1 Introduction
6.1.2 Related Work
6.1.3 Proposed Method
6.1.4 Experimental Results
6.1.5 Conclusion
6.2 Combination of Interacting Multiple Models with the Particle Filter for Three-dimensional Target Tracking in Underwater Wireless Sensor Networks
6.2.1 Introduction
6.2.2 Problem Formulation
6.2.3 IMMPF Underwater Target Tracking Algorithm
6.2.4 Simulation and Results
6.2.5 Conclusion
References
Chapter 7 Video Image Processing for River Surface Velocimetry
7.1 Balloon-borne Spectrum-polarization Imaging for River Surface Velocimetry under Extreme Conditions
7.1.1 Introduction
7.1.2 Near-infrared Spectrum-polarization Imaging for Flow Tracers Detection
7.1.3 Balloon-borne Low-altitude Telemetry System with A Self-stabilized Servo Platform
7.1.4 Experimental Results
7.1.5 Conclusion
7.2 An Information Acquisition Method Based on Dragonfly Vision Mechanism for Observed Target Displacement Measurement
7.2.1 Introduction
7.2.2 Related Work
7.2.3 Optical Characteristics of River Surface
7.2.4 Our Approach
7.2.5 Experimental Results
7.2.6 Conclusion
References
Chapter 8 Remote Sensing Imagery Processing and Analysis
8.1 Multi-class Remote Sensing Objects Recognition B