Multidimensional Signal Processing

Qionghai Dai, Tsinghua University, Beijing, China

Table of Content:
Chapter 1 Review on multidimensional signal processing
1.1 Introduction
1.2 Multi-dimensional signal: fast transformation
1.3 Multi-dimensional signal: sparse representation
1.4 Multi-dimensional signal: low rank analysis
1.5 Summary
Chapter 2 Multi-dimensional discrete cosine transform matrix and fast decomposition
2.1 Introduction
2.2 Dct transformation and matrix decomposition
2.3 M ddct and m d ratio
2.4 M d ratio dct fast algorithm
2.5 Computational complexity comparison
2.6 Summary
Chapter 3 Multidimensional discrete wavelet transform: vlsi architecture
3.1 Introduction
3.2 Multi-dimensional dwt transformation framework
3.3 Comparison and evaluation
3.4 Summary
Chapter 4 Multi-dimensional signal: sparse representation theory and application
4.1 Introduction
4.2 Compression perception
4.3 Application of compression perception
4.4 Summary
Chapter 5 Discrete wavelet transform based on image/video coding
5.1 Introduction
5.2 Dual-tree discrete wavelet transform
5.3 Image coding based on ddwt
5.4 Adaptive DWTWT
5.5 Image/Video Encoding Based on addwp
5.6 Summary
Chapter 6 Low-order analysis of multi-dimensional signal: theory and application
6.1 Introduction
6.2 Matrix rank
6.3 Matrix low rank sparse decomposition
6.4 Applications and examples
6.5 Summary
Chapter 7 Sparse structure visual information perception
7.1 Introduction
7.2 Logarithms and heuristic perception algorithm
7.3 Log sum approximation in the data analysis application
7.4 Log sum approximation in stereo reconstruction application
7.5 Summary
7.6 Appendix
Chapter 8 Dynamic reconstruction of the dynamic topography
8.1 Introduction
8.2 Research updates
8.3 3D reconstruction of dynamic scene based on 3D motion estimation
8.4 Experimental results and analysis
8.5 Summary
Chapter 9 Low-rank decomposition of multidimensional signal and adaptive reconfiguration
9.1 Foreword
9.2 Low-rank accumulate matrix construction and low-rank decomposition of multidimensional signal 9.3 Applications of low rank decomposition in compressive perception image reconfiguration
9.4 Application of low rank decomposition in super resolution
9.5 Summary
References