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![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)
Uploaded byJia-Bin Huang
Image Smoothing for Structure Extraction
The document discusses image smoothing techniques for structure extraction. It aims to achieve edge-aware smoothing while distinguishing texture from structure. Previous related work includes Gaussian blurring, L0 gradient minimization, and domain transformations. The proposed algorithm formulates smoothing as a global optimization problem that minimizes the data term and total variation regularization term. It uses a Huber loss function and iterative reweighted L1 norm to encourage sparsity. Test results will be conducted using source code from previous works. Future work includes implementing the algorithm in CVX and testing effectiveness.
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- 1. Image Smoothing ForStructure 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