Image Blending
W ES L E Y N A S LU N D
CO LO R A D O S C H O O L O F M I N ES
D EC E M B E R 2 , 2 0 1 3
How to put bears in your hot tub (safely)
What’s my motivation?
PHOTO SPHERE
IMAGE STITCHING
Registration
◦ Match features
Calibration
◦ Correct lens distortion, exposure differences, vignetting, etc.
Blending
◦ Seamline
◦ Fusion
Simplify the problem
SATELLITE LANDSAT IMAGES
GLOBAL ORTHO AERIAL IMAGES
Assumptions
IMAGE STITCHING
Registration
◦ Match features
Calibration
◦ Correct lens distortion, exposure differences, vignetting, etc.
Blending
◦ Seamline
◦ Fusion
Seamline Blending
Simple Solution: Feathering
,
1 , ,
∗ 1 ,
2 , ,
1
∗ 2 ,
Seamline Blending
Better: Multiresolution spline (Pyramid Blending)
• Avoids blurring, doubling
• Computationally efficient
Seamline Blending
Color images
• “Exposure” differences still an issue
• Resolved later
• Could use radiance maps to balance
Seamline Blending
Alternate method: Optimal Seamline Cut
• Apply Dijkstra’s Algorithm
• Cost map from cross correlation
• Find least cost path (“avoid obstacles”)
1
→ 0
→ 1
Image Fusion
• Methods
• Average
• Details obscured
• Laplacian Pyramid
• Spectral detail loss
• Wavelet
Analogy: Logo is Landsat image
Apple is Ortho Aerial image
Image Fusion
Wavelet Families: Properties
Mother
Father
Daughter
Image Fusion
Why do these properties matter?
◦ Discrete Wavelet Transform (DWT)
◦ Wavelet family provides orthonormal basis
Image Fusion
Wavelet Fusion
◦ Spatial detail contained in high frequencies
◦ Decomposition extracts detail
◦ Several combination methods
◦ Substitutive
◦ Low resolution detail replace with high resolution detail
◦ Additive
◦ Add detail coefficients
◦ Weighted
◦ Based on mean/standard deviation or other criteria
Image Fusion
MATLAB’s wavemenu
GUI for wfusimg()
◦ Look at source code:
edit wfusimg
Comparing methods
◦ Wavelets (Haar)
◦ Fusion methods
Image Fusion
Low Detail (1)
High Detail (2)
Max, Max
Image Fusion
Decomposition Comparison
1 Level
3 Levels
5 Levels
Image Fusion
Want image 1 consistency and image 2 detail Max, Max
Max, Mean
Max, img2
Image Fusion
Quantitative Comparisons
◦ Entropy
◦ Higher entropy, higher detail
◦ Correlation coefficient
◦ How close to original the fused image is
◦ Warping
◦ Measures spectral distortion
Image Fusion
Approximation Detail
max
max
max
mean
max
img2
Level
2
2
2
Entropy
5.002327
4.825146
4.711708
Image
Entropy
Logo
3.61948
Apple
5.150525
Correlation
0.725695
0.721694
0.70458
Warping
0.629166
0.398735
0.888424
Image Fusion
Satellite Landsat Image
Global Ortho Aerial Image
Image Fusion
SATELLITE LANDSAT IMAGES
GLOBAL ORTHO AERIAL IMAGES
Image Fusion
Max, Max
Max, Img2
Improvements/Future Work
Fusion methods
◦ Mathematical formulation is limiting for weighted methods
◦ Need inseparable filters for best results
◦ Quantitative comparisons are somewhat misleading
◦ Methods are not standardized
◦ Difficult to compare directly
◦ Compare results of different wave families
◦ Haar wavelets are approximations of Daubechies wavelets
Questions?