image smoothing for structure extraction
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Image Smoothing For Structure Extraction Linjia Chang, lchang10@illinois.edu Mentor: Jia-Bin Huang, jbhuang1@illinois.edu
Research Symposium
Applications Goal
Methods
· Detail enhancement
· Image composition
· Object recognition
· Image denoise
· Re-coloring
· Stylization
· Video segmentation
· Structure extraction
· Optimization with total variation regularization
· - Robust loss function for texture removal
· - Iterative reweighted L1 for sparsity[3]
Things learnt from P.U.R.E. Future Work And Reference
Algorithm
Previous Related Work
·Achieve Edge-aware image
smoothing while being able to
distinguish texture/structure from
general natural images
· Domain Transformation[1] · L0 Gradient Minimization · Structure Texture Extraction[2] · Gaussian Blur
Through the research this semester, I learnt:
1.How to find/read/classify a paper in related fields.
2. How to conduct a complete research from the
beginning to the end.
3. The importance of doing experiments and testing
everything on my own.
Special thanks to: Mentor Jia-Bin Huang
P.U.R.E. Committee
· Idea: Image smoothing as a global optimization problem
Minimize S* = argmin ∑ λ||Sp – Ip|| + w||▽Sp|| s
Pixel = weighted
average of
its neighbors
Preserves the original distance:
isometric transform
A major edge in a
local window
contributes more
similar-direction
gradients
Enhances high-contrast edges by
confining numbers of non-zero gradients
Future works includes:
1.Using CVX to solve for the final algorithm
2.Testing algorithm effectiveness and efficiency
Reference:
[1] Eduardo S. L. Gastal and Manuel M. Oliveira. "Domain
Transform for Edge-Aware Image and Video Processing".
SIGGRAPH 2011.
[2]Li Xu, et al. "Structure Extraction from Texture via Natural
Variation Measure”. SIGGRAPH Asia 2012
[3]Candes, E.J., et al. “Enhancing Sparsity by Reweighted ℓ1
Minimization”. Journal of Fourier Analysis and Applications,
2008
[4]Tom Goldstein, et al. “The Split Bregman Method for L1-
Regularized Problems”. SIAM Journal on Imaging
Sciences, 2009
First solve the part without the
weight = λ||▽Sp||
And then introduce weight w
Iteratively Reweighted L1 (Encourage Sparsity)
Solution Algorithm[4]
1. Set dummy variables u and v
S* = argmin ∑λ||Sp – Ip|| + w(|u|+|v|)+ β|(▽Spx-u)²+ (▽Spy-v)²|
w=1 / (|▽Sp| + ε) Test results using source code given by previous works
Similar as previous works but using Huber LF
Data Term Regularization Term
Huber Loss Function
s
2. Fix u, v and solve for S (convex)
3. Fix S and solve for u, v (shrinkage)
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