Intro
Preface
Organization
Contents
Image Processing and Enhancement Techniques
Underwater Image Enhancement and Restoration Techniques: A Comprehensive Review, Challenges, and Future Trends
1 Introduction
2 Underwater Image Enhancement and Restoration
2.1 Challenges of Underwater Imaging
2.2 Underwater Image Enhancement
2.3 Underwater Image Restoration
3 Underwater Image Datasets and Quality Metrics
4 Experiments and Analysis
5 Opportunities and Future Trends
6 Conclusion
References

A Self-supervised Learning Reconstruction Algorithm with an Encoder-Decoder Architecture for Diffuse Optical Tomography
1 Introduction
2 Method
2.1 Light Propagation Model
2.2 Deep Convolutional Encoder-Decoder Architecture
3 Results
3.1 Dataset
3.2 Reconstruction Results
4 Conclusions
References
TSR-Net: A Two-Step Reconstruction Approach for Cherenkov-Excited Luminescence Scanned Tomography
1 Introduction
2 Methods
2.1 Forward Problem
2.2 Inverse Problem
2.3 Two-Step Reconstruction Algorithm
3 Results
3.1 Reconstruction Depth Test

3.2 Spatial Resolution Test
3.3 Generalization Ability Test
4 Discussion and Conclusions
References
A Method for Enhancing the Quality of Compressed Videos Based on 2D Convolution and Aggregating Spatio-Temporal Information
1 Introduction
2 Related Work
2.1 Unet
2.2 Attention Mechanisms
3 The Proposed Approach
3.1 Method Proposal
3.2 Framework
3.3 Conv-block Module
4 Experiments
4.1 Experimental Setup
4.2 Dataset
4.3 Comparison to State-of-the-Arts
4.4 Analysis and Discussions
5 Conclusion
References

Multimedia-Based Informal Learning in Museum Using Augmented Reality
1 Introduction
2 Related Work
2.1 Cognitive Theory of Multimedia Learning in AR
2.2 AR in Museum Learning
3 Method
3.1 Environment Scenario
3.2 Experiment Design
3.3 Experiment Procedure
3.4 Participant
4 Result
4.1 Task Load
4.2 Cognitive Load
4.3 Acquired Knowledge
4.4 User Preference
5 Discussion
6 Conclusions and Future Work
References
Machine Vision and 3D Reconstruction
MAIM-VO: A Robust Visual Odometry with Mixed MLP for Weak Textured Environment
1 Introduction

2 Related Work
3 Method
3.1 System Architecture
3.2 Mixer-WMLP Architecture
4 Experiments
4.1 Feature Matching
4.2 Localization on TUM RGB-D Dataset
5 Conclusions
References
Visual SLAM Algorithm Based on Target Detection and Direct Geometric Constraints in Dynamic Environments
1 Introduction
2 Related Works
2.1 Indirect Geometric Constraint Method
2.2 Direct Geometric Constraint Method
3 Method
3.1 System Overview
3.2 Dynamic Feature Detection Based on Target Detection and Direct Geometric Constraints