Saliency Detection via Divergence Analysis: A Unified Perspective ICPR 2012
Image Smoothing for Structure Extraction
1. Image Smoothing For Structure Extraction
Linjia Chang, lchang10@illinois.edu Mentor: Jia-Bin Huang, jbhuang1@illinois.edu
Goal Applications
·Achieve Edge-aware image · Detail enhancement · Re-coloring
smoothing while being able to · Image composition · Stylization
distinguish texture/structure from · Object recognition · Video segmentation
general natural images · Image denoise · Structure extraction
Methods
· Optimization with total variation regularization
· - Robust loss function for texture removal
· - Iterative reweighted L1 for sparsity[3]
Previous Related Work
· Gaussian Blur · L0 Gradient Minimization · Domain Transformation[1] · Structure Texture Extraction[2]
Pixel = weighted
average of
its neighbors
A major edge in a
local window
contributes more
Enhances high-contrast edges by Preserves the original distance: similar-direction
confining numbers of non-zero gradients isometric transform gradients
Algorithm
· Idea: Image smoothing as a global optimization problem
Huber Loss Function Minimize S* = argmin ∑ λ||Sp – Ip|| + w||▽Sp||
s
Data Term Regularization Term
Similar as previous
works but using Huber
Iteratively Reweighted L1
Solution Algorithm[4]
LF (Encourage Sparsity)
1. Set dummy variables u and v First solve the part without the
S* = argmin ∑λ||Sp – Ip|| + w(|u|+|v|)+ β|(▽Spx-u)²+ (▽Spy-v)²| weight = λ||▽Sp||
s
And then introduce weight w
2. Fix u, v and solve for S (convex)
3. Fix S and solve for u, v (shrinkage) w=1 / (|▽Sp| + ε)
Test results using source code given by previous works
Things learnt from P.U.R.E. Future Work And Reference
Through the research this semester, I learnt: Future works includes:
1.Using CVX to solve for the final algorithm
1.How to find/read/classify a paper in related fields. 2.Testing algorithm effectiveness and efficiency
2. How to conduct a complete research from the Reference:
beginning to the end. [1] Eduardo S. L. Gastal and Manuel M. Oliveira. "Domain
Transform for Edge-Aware Image and Video Processing".
3. The importance of doing experiments and testing SIGGRAPH 2011.
everything on my own. [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
Special thanks to: Mentor Jia-Bin Huang Minimization”. Journal of Fourier Analysis and Applications,
P.U.R.E. Committee 2008
[4]Tom Goldstein, et al. “The Split Bregman Method for L1-
Regularized Problems”. SIAM Journal on Imaging
Research Symposium Sciences, 2009
![Image Smoothing For Structure Extraction
Linjia Chang, lchang10@illinois.edu Mentor: Jia-Bin Huang, jbhuang1@illinois.edu
Goal Applications
·Achieve Edge-aware image · Detail enhancement · Re-coloring
smoothing while being able to · Image composition · Stylization
distinguish texture/structure from · Object recognition · Video segmentation
general natural images · Image denoise · Structure extraction
Methods
· Optimization with total variation regularization
· - Robust loss function for texture removal
· - Iterative reweighted L1 for sparsity[3]
Previous Related Work
· Gaussian Blur · L0 Gradient Minimization · Domain Transformation[1] · Structure Texture Extraction[2]
Pixel = weighted
average of
its neighbors
A major edge in a
local window
contributes more
Enhances high-contrast edges by Preserves the original distance: similar-direction
confining numbers of non-zero gradients isometric transform gradients
Algorithm
· Idea: Image smoothing as a global optimization problem
Huber Loss Function Minimize S* = argmin ∑ λ||Sp – Ip|| + w||▽Sp||
s
Data Term Regularization Term
Similar as previous
works but using Huber
Iteratively Reweighted L1
Solution Algorithm[4]
LF (Encourage Sparsity)
1. Set dummy variables u and v First solve the part without the
S* = argmin ∑λ||Sp – Ip|| + w(|u|+|v|)+ β|(▽Spx-u)²+ (▽Spy-v)²| weight = λ||▽Sp||
s
And then introduce weight w
2. Fix u, v and solve for S (convex)
3. Fix S and solve for u, v (shrinkage) w=1 / (|▽Sp| + ε)
Test results using source code given by previous works
Things learnt from P.U.R.E. Future Work And Reference
Through the research this semester, I learnt: Future works includes:
1.Using CVX to solve for the final algorithm
1.How to find/read/classify a paper in related fields. 2.Testing algorithm effectiveness and efficiency
2. How to conduct a complete research from the Reference:
beginning to the end. [1] Eduardo S. L. Gastal and Manuel M. Oliveira. "Domain
Transform for Edge-Aware Image and Video Processing".
3. The importance of doing experiments and testing SIGGRAPH 2011.
everything on my own. [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
Special thanks to: Mentor Jia-Bin Huang Minimization”. Journal of Fourier Analysis and Applications,
P.U.R.E. Committee 2008
[4]Tom Goldstein, et al. “The Split Bregman Method for L1-
Regularized Problems”. SIAM Journal on Imaging
Research Symposium Sciences, 2009](https://image.slidesharecdn.com/imgsmoothingposterfall2012-121204143827-phpapp02/85/image-smoothing-for-structure-extraction-1-320.jpg)
