Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Image Smoothing for Structure Extraction


Published on

  • Be the first to comment

  • Be the first to like this

Image Smoothing for Structure Extraction

  1. 1. Image Smoothing For Structure Extraction Linjia Chang, 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