A FAST AND ROBUST FRAMEWORK FOR IMAGE FUSION AND ...

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Acknowledgements This work is the result of four and a half years of close collaboration with a unique team of scientists and friends. It was their sincere assistance and support that helped me reach this milestone. First and foremost, I would like to thank my advisor Professor Peyman Milanfar, my role model of an exceptional scientist and teacher. It was a great privilege and honor to work and study under his guidance. I would also like to thank him for his friendship, empathy, and great sense of humor (and for introducing me to French press coffee). I am grateful to my mentor Professor Michael Elad, for generously sharing his intellect, and ideas. His smiling face, patience, and careful comments were constant sources of encouragement. I would like to thank Dr. Julian Christou, for invaluable comments and feedback throughout these years and Professor Ali Shakouri for serving on my committee and reviewing this thesis. I am grateful to all UCSC professors specially Professors Benjamin Friedlander, Roberto Manduchi, Claire Max, Hai Tao, Donald Wiberg, and Michael Issacson. As the electri- cal engineering GSA representative for the last few years, I had the opportunity of interacting with Dean Steve Kang; I am thankful to him and Vice Provost Lisa Sloan for going the extra mile, making the graduate studies a more pleasant experience for the UCSC graduate students. I am thankful to mes professeurs préférés Hervé Le Mansec, Greta Hutchison, Miriam Ellis, and Angela Elsey in the French department. I owe a lot to my former M.Sc. advisors Pro- fessors Caro Lucas and Fariba Bahrami. I would like to say a big thank-you to the Oracle of the School of Engineering Carol Mullane and the wonderful UCSC staff, specially Jodi Rieger, Carolyn Stevens, Ma Xiong, Meredith Dyer, Andrea Legg, Heidi McGough, Lynne Sheehan, Derek Pearson, David Cosby, and Marcus Thayer. Also, I would like to thank the Center for Adaptive Optics (CFAO) for supporting and funding this research. Thanks to my friends and colleagues of many years, Ali and “the-other” Sina, for their unconditional friendship and support. Many thanks are due to Dirk and Emily for always being there for me, and to Saar for his friendship and unreserved honesty. xvi
I thank all the handsome gentlemen of “Da Lab”, XiaoGuang, Amyn, Lior, Hiro, Mike, and Davy. Thanks to the friends and family, Maryam and the Pirnazar family, Saeedeh, Reza and Shapour, and the one and only Mehrdad for their support. My special thanks goes to a good friend and my favorite submarine captain Reay, and to Lauren (I will never forget the yummy thanksgiving dinners). I thank my favorite direc- tor/producer David and Kelly the best lawyer in the west. Thanks to the “Grill Master” Nate for being a constant source of surprise and entertainment. I thank Marco and Teresa for the “de- cent” pasta nights, and of course I am grateful for the friendship of the “Catholic cake slicing theory” architect and my favorite comedy critic, Mariëlle. Finally, my thanks goes to the beautiful city of Santa Cruz and its lovely people for being so hospitable to me. xvii
Page 1 and 2: UNIVERSITY OF CALIFORNIA SANTA CRUZ
Page 3 and 4: Contents List of Figures vi List of
Page 5 and 6: Appendix F Appendix: Affine Motion
Page 7 and 8: 2.3 Effect of upsampling D T matrix
Page 9 and 10: 3.11 Multi-frame color super-resolu
Page 11 and 12: 4.5 A sequence of 250 low-resolutio
Page 13 and 14: List of Tables 2.1 The true motion
Page 15: show that using such constraints wi
Page 19 and 20: It’s the robustness, stupid! xix
Page 21 and 22: quality. We develop the theory and
Page 23 and 24: That is, ⎧ ⎪⎨ ⎪⎩ y1 = h1.
Page 25 and 26: problem of super-resolution is line
Page 27 and 28: accomplished by way of Lagrangian t
Page 29 and 30: which addresses the artifacts resul
Page 31 and 32: noise and Y is the resulting discre
Page 33 and 34: ods, where the regularization-like
Page 35 and 36: 2.2 Robust Super-Resolution 2.2.1 R
Page 37 and 38: and horizontal directions and subsa
Page 39 and 40: Note that if p =2then (2.8) will be
Page 41 and 42: Figure 2.3: Effect of upsampling D
Page 43 and 44: where λ, the regularization parame
Page 45 and 46: Although a relatively large regular
Page 47 and 48: image(X). Figure 2.5(b) is the corr
Page 49 and 50: of high-resolution frame � Xn. Bl
Page 51 and 52: as conjugate gradient is not a stra
Page 53 and 54: of minimization problem (2.23) can
Page 55 and 56: norm is not robust to motion error,
Page 57 and 58: e one. Figure 2.12(f) is the result
Page 59 and 60: 50 100 150 200 250 300 350 50 100 1
Page 61 and 62: a: One of 8 LR Frames b: Cubic Spli
Page 63 and 64: a: Frame 1 of 55 LR Frames b: Frame
Page 65 and 66: Chapter 3 Multi-Frame Demosaicing a
Page 67 and 68:
chrominance layers are separated fr
Page 69 and 70:
Other examples of popular demosaici
Page 71 and 72:
a: Original b: Down-sampled c: Blur
Page 73 and 74:
3.3 Mathematical Model and Solution
Page 75 and 76:
epresents the down-sampling operato
Page 77 and 78:
image (Spatial Luminance Penalty Te
Page 79 and 80:
Acquire a Set of LR Color Filtered
Page 81 and 82:
espect to the other color channels.
Page 83 and 84:
in different color bands. Our propo
Page 85 and 86:
the robust super-resolution method
Page 87 and 88:
We used the method described in [48
Page 89 and 90:
the color artifacts of a set of low
Page 91 and 92:
a: Reconst. with lumin. regul. b: R
Page 93 and 94:
a: Reconst. with all terms. Figure
Page 95 and 96:
a: LR b: Shift-and-Add c: SR [4] on
Page 97 and 98:
a b c d e f Figure 3.14: Multi-fram
Page 99 and 100:
a b c d Figure 3.16: Multi-frame co
Page 101 and 102:
a b c d e f Figure 3.18: Multi-fram
Page 103 and 104:
ates from them in several important
Page 105 and 106:
order. The current image X(t) is of
Page 107 and 108:
With this alternative definition of
Page 109 and 110:
GS a 0 0 0 b 0 0 0 c D T GSD =⇒
Page 111 and 112:
Figure 4.2: Block diagram represent
Page 113 and 114:
ˆZ(t), necessitating a further joi
Page 115 and 116:
Figure 4.3: Block diagram represent
Page 117 and 118:
4.3 Simultaneous Deblurring and Int
Page 119 and 120:
Y (t) Input CFA Data Y R (t) Y G (t
Page 121 and 122:
4.5(b) & 4.5(f) show frames #50 and
Page 123 and 124:
a b c d e f g h Figure 4.6: A seque
Page 125 and 126:
dB 24 22 20 18 16 14 12 10 8 0 50 1
Page 127 and 128:
a b c d e f g h Figure 4.10: A sequ
Page 129 and 130:
Chapter 5 Constrained, Globally Opt
Page 131 and 132:
In this chapter, we study such prio
Page 133 and 134:
(a) (b) Figure 5.2: The consistent
Page 135 and 136:
vector field δi,j is spatially con
Page 137 and 138:
5.3.3 Robust Multi-Frame Registrati
Page 139 and 140:
MSE 10 −1 10 −2 10 −3 10 0 10
Page 141 and 142:
(a) (b) (c) (d) Figure 5.6: Experim
Page 143 and 144:
we advocated the use of the L1 norm
Page 145 and 146:
y the user. - The user is able to s
Page 147 and 148:
There is need for more research on
Page 149 and 150:
than the corresponding single-frame
Page 151 and 152:
In [111], Elad proved that such fil
Page 153 and 154:
all iterations, which means estimat
Page 155 and 156:
Appendix C Noise Modeling Based on
Page 157 and 158:
Appendix D Error Modeling Experimen
Page 159 and 160:
Appendix E Derivation of the Inter-
Page 161 and 162:
Appendix F Appendix: Affine Motion
Page 163 and 164:
Bibliography [1] A. Zomet, A. Rav-A
Page 165 and 166:
[26] H. Ur and D. Gross, “Improve
Page 167 and 168:
[55] K. Hirakawa and T. Parks, “A
Page 169 and 170:
[84] S. Farsiu, D. Robinson, M. Ela
Page 171:
[112] S. M. Kay, Fundamentals of st
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