Contents......Page 8
List of Figures......Page 12
List of Tables......Page 16
Preface......Page 18
Acknowledgments......Page 20
1 Introduction......Page 22
1.1 Characteristics of Remotely Sensed Imagery......Page 24
1.1.1 Multi-spectral images......Page 29
1.1.2 Panchromatic image......Page 31
1.1.3 Hyper-spectral images......Page 33
1.2 Low Spatial Resolution Imaging......Page 35
1.3 Image Fusion in Remotely Sensed Images......Page 38
1.4 Multi-resolution Image Fusion: An Ill-posed Inverse Problem......Page 39
1.5 Indian Remote Sensing Satellites......Page 41
1.6 Applications of Image Fusion......Page 43
1.8 Organization of the Book......Page 46
2 Literature Review......Page 48
2.1 Projection Substitution Based Techniques......Page 50
2.2 Multi-resolution Based Techniques......Page 55
2.3 Model Based Fusion Approaches......Page 62
2.4 Hyper-spectral Sharpening Methods......Page 69
2.5 Conclusion......Page 71
3 Image Fusion Using Different Edge-preserving Filters......Page 73
3.1 Related Work......Page 74
3.2 Fusion Using Multistage Guided Filter (MGF)......Page 75
3.2.1 Multistage guided filter (MGF)......Page 76
3.2.2 Proposed approach using guided filter......Page 78
3.3 Fusion Approach Using Difference of Gaussians (DoGs)......Page 80
3.3.1 Difference of Gaussians (DoGs)......Page 81
3.3.2 Proposed approach using DoGs......Page 82
3.4 Experimental Illustrations......Page 84
3.4.1 Experimentations: Ikonos-2 dataset......Page 88
3.4.2 Experimentations: Quickbird dataset......Page 92
3.4.3 Experimentations: Worldview-2 dataset......Page 95
3.5 Conclusion......Page 99
4 Image Fusion: Model Based Approach with Degradation Estimation......Page 101
4.1 Previous Works......Page 102
4.2 Description of the Proposed Approach Using Block Schematic......Page 104
4.4 Contourlet Transform Based Initial Approximation......Page 106
4.5 Forward Model and Degradation Estimation......Page 109
4.6 MRF Prior Model......Page 112
4.7.1 MAP estimation......Page 115
4.7.2 Optimization process......Page 116
4.8 Experimentations......Page 117
4.8.1 Effect of decimation matrix coefficients on fusion......Page 121
4.8.2 Effect of MRF parameter ฮณm on fusion......Page 123
4.8.3 Fusion results for degraded dataset: Ikonos-2......Page 124
4.8.4 Fusion results for degraded dataset: Quickbird......Page 133
4.8.5 Fusion results for degraded dataset: Worldview-2......Page 141
4.8.6 Fusion results for un-degraded (original) datasets: Ikonos-2, Quickbird and Worldview-2......Page 147
4.8.7 Spectral distortion at edge pixels......Page 154
4.8.8 Computational time......Page 158
4.9 Conclusion......Page 160
5 Use of Self-similarity and Gabor Prior......Page 161
5.1 Related Work......Page 162
5.2 Block Schematic of the Proposed Method......Page 164
5.3 Initial HR Approximation......Page 165
5.4 LR MS Image Formation Model and Degradation Matrix Estimation......Page 171
5.5 Regularization Using Gabor and MRF Priors......Page 173
5.5.1 Optimization process......Page 176
5.6 Experimental Results......Page 177
5.6.1 Experimental setup......Page 179
5.6.2 Experimental results on degraded and un-degraded Ikonos-2 datasets......Page 180
5.6.3 Experimental results on degraded and un-degraded Quickbird datasets......Page 185
5.6.4 Experimental results on degraded and un-degraded Worldview-2 datasets......Page 190
5.6.5 Comparison of fusion results with CS and TV based approaches......Page 195
5.6.6 Computation complexity......Page 199
5.7 Conclusion......Page 200
6 Image Fusion: Application to Super-resolution of Natural Images......Page 201
6.1 Related Work......Page 202
6.2 Estimation of Close Approximation of the SR Image......Page 205
6.3 Refining SR Using MAPโMRF Framework......Page 209
6.4 MRF Prior and SR Regularization......Page 211
6.4.1 Optimization process......Page 212
6.5 Experimental Demonstrations......Page 213
6.5.1 SR results on gray scale images......Page 214
6.5.2 SR results on color images......Page 218
6.6 Conclusion......Page 223
7.1 Conclusion......Page 224
7.2 Future Research Work......Page 228
Bibliography......Page 232