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Solving Poisson Equations Using Least Square Technique in Image Editing Colin Zheng Yi Li.

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Presentation on theme: "Solving Poisson Equations Using Least Square Technique in Image Editing Colin Zheng Yi Li."— Presentation transcript:

1 Solving Poisson Equations Using Least Square Technique in Image Editing
Colin Zheng Yi Li

2 Roadmap Poisson Image Editing Least Square Techniques Poisson Blending
Poisson Matting Least Square Techniques Conjugate Gradient With Pre-conditioning Multi-grid

3 Intro to Blending source target paste blend

4 Gradient Transfer

5 Gradient Transfer

6 Gradient Transfer

7 Gradient Transfer

8 Results

9 Results

10 ∇I = (F −B)∇α+ α∇F +(1− α)∇B
Into to Matting I = α F + (1 – α) B ∇I = (F −B)∇α+ α∇F +(1− α)∇B ∇I ≈ (F −B)∇α

11 Poisson Matting with

12 Poisson Matting with with

13 Results

14 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

15 Conjugate Gradient +

16 Conjugate Gradient: Starting
Initialized as the source image (50 iterations) Initialized as the target image (50 iterations)

17 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

18 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.

19 Precondition

20 Precondition (Cont) Step=0 Step=5 Step=10 Step=20 Step=40 Without

21 Precondition Demo (20 iterations)

22 Multigrid Use coarse grids to computer an improved initial guess for the fine-grid.

23 Multigrid: Different Starting
Initialized as Target (bad starting)

24 Multigrid (Cont) Looser threshold for the coarse grids:

25 Multigrid + Precondition
Combine Multigrid with Precondition

26 Multigrid Demo

27 Conclusion Applications Least Square Techniques Performance Analysis
Poisson Blending Poisson Matting Least Square Techniques Conjugate Gradient With Pre-conditioning Multi-grid Performance Analysis Sensitivity Convergence


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