solving poisson equations using least square technique in image editing
DESCRIPTION
Solving Poisson Equations Using Least Square Technique in Image Editing. Colin Zheng Yi Li. Roadmap. Poisson Image Editing Poisson Blending Poisson Matting Least Square Techniques Conjugate Gradient With Pre-conditioning Multi-grid. Intro to Blending. source. target. paste. blend. - PowerPoint PPT PresentationTRANSCRIPT
Solving Poisson Equations Using Least Square Technique in
Image Editing
Colin Zheng
Yi Li
Roadmap
• Poisson Image Editing– Poisson Blending– Poisson Matting
• Least Square Techniques– Conjugate Gradient – With Pre-conditioning– Multi-grid
Intro to Blending
source target paste blend
Gradient Transfer
Gradient Transfer
Gradient Transfer
Gradient Transfer
Results
Results
Into to Matting
I = α F + (1 – α) B
∇I = (F −B) α+ α F +(1− α) B∇ ∇ ∇
∇I ≈ (F −B) α∇
Poisson Matting
with
Poisson Matting
with
with
Results
Conjugate Gradient Method
• Problem to solve: Ax=b• Sequences of iterates:
x(i)=x(i-1)+(i)d(i)
• The search directions are the residuals.• The update scalars are chosen to make
the sequence conjugate (A-orthogonal)• Only a small number of vectors needs to
be kept in memory: good for large problems
Conjugate Gradient
+
Conjugate Gradient: Starting•Initialized as the source image
(50 iterations)
•Initialized as the target image
(50 iterations)
Precondition
• We can solve Ax=b indirectly by solving
M-1Ax= M-1b
• If (M-1A) << (A), we can solve the latter equation more quickly than the original problem.
* If max and min are the largest and smallest eigenvalues of a symmetric positive definite matrix B, then the spectral condition
number of B is
min
max
Symmetric Successive Over Relaxation (SSOR)
Barrett, R.; Berry, M.; Chan, T. F.; Demmel, J.; Donato, J.; Dongarra, J.; Eijkhout, V.; Pozo, R.; Romine, C.; and Van der Vorst, H. Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, 2nd ed. Philadelphia, PA: SIAM, 1994.
Precondition
0.01
0.1
1
10
100
1000
10000
0 25 50 75 100
Iteration
Res
idu
al (
log
)
source target source, SSOR
Precondition (Cont)
WithoutPrecondition
WithoutPrecondition
Step=0 Step=5 Step=10 Step=20 Step=40
Precondition Demo(20 iterations)
Multigrid
Use coarse grids to computer an improved initial guess for the fine-grid.
0.01
1
100
10000
0 50 100 150 200 250
iteration
|r|
Multigrid Precondition C.G.
Multigrid: Different Starting
Initialized as Target (bad starting)
0.01
1
100
10000
0 50 100 150 200 250
iteration
|r|
Multigrid CG Precondition
Multigrid (Cont)
Looser threshold for the coarse grids:
0.01
1
100
10000
0 50 100 150 200 250
iteration
|r|
Multigrid Precondition Multigrid (loose T)
Multigrid + Precondition
Combine Multigrid with Precondition
0.01
1
100
10000
0 10 20 30 40 50 60 70
iteration
|r|
Precondition Multigrid+Precondition
Multigrid Demo
Conclusion
• Applications– Poisson Blending– Poisson Matting
• Least Square Techniques– Conjugate Gradient – With Pre-conditioning– Multi-grid
• Performance Analysis– Sensitivity– Convergence