- 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 and 16: show that using such constraints wi
- Page 17: I thank all the handsome gentlemen
- 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