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Image Smoothing for Structure Extraction

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Image Smoothing for Structure Extraction

  1. 1. Image Smoothing For Structure Extraction Linjia Chang, lchang10@illinois.edu Mentor: Jia-Bin Huang, jbhuang1@illinois.eduGoal 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 gradientsAlgorithm · 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 worksThings learnt from P.U.R.E. Future Work And ReferenceThrough the research this semester, I learnt: Future works includes: 1.Using CVX to solve for the final algorithm1.How to find/read/classify a paper in related fields. 2.Testing algorithm effectiveness and efficiency2. 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 ℓ1Special 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

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