Enough filter parameters contents introduc tion the

  • No School
  • AA 1
  • 15

This preview shows page 5 - 9 out of 15 pages.

Enough!!! Filter parameters CONTENTS: INTRODUC TION THE BAYESIAN NONLOCAL FILTER INTERACTIV E GENETIC ALGORITHM IMPLEMENTA TION ISSUES & RESULTS CONCLUSIO NS
Image of page 5
EBNL FILTER (Enhanced Bayesian Non Local Filter) EBNL FILTER (Enhanced Bayesian Non Local Filter) 6 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma… CONTENTS: INTRODUCTI ON THE BAYESIAN NONLOCAL FILTER INTERACTIV E GENETIC ALGORITHM IMPLEMENTA TION ISSUES & RESULTS CONCLUSIO NS It is the extension of the Bayesian Non Local means filter and, it minimizes the Bayesian risk under the assumption that the statistical estimations from an image patch, are valid for the true involved statistical parameters. It uses pixel preselection. u: noise-free image ; v:noisy image. The BNL filter updates the noisy data at pixel v(x) : Under the assumption of fully developed and non- correlated speckle samples, p(v(x)|u(y)), can be estimated by the Gamma distribution M x M local neighborhood N x N observation patch M x M N N ρ = k /√L; k 2 Filter parameters to be optimized
Image of page 6
7 CONTENTS: INTRODUCTI ON THE BAYESIAN NONLOCAL FILTER INTERACTIV E GENETIC ALGORITHM IMPLEMENTA TION ISSUES & RESULTS CONCLUSIO NS Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma… Preselection Preselection EBNL Filter Design parameters: It looks like a chromosome! To account for the pixel preselection, the observation patch N x N is defined as: To eliminate correlated pixels (ϒ threshold parameter), ϒ < 1 Pixels with an intensity value higher than I max /2 are preselected through the sigma range mechanism N(x) = ε(x) ∩ N 1 (x) ∩ N 2 (x) y ∊ N 2 (x), only if v(y) ∊ (u’(x)·I 1 , u’(x)·I 2 ) N x N observation patch M x M N N Uncorrelated pixels k ϒ ξ n SM SN Number of Looks Sigma mechanism Max. Number of iterations M x M size patch of x neigborhood N x N size patch of observation window
Image of page 7
INTERACTIVE GENETIC ALGORITHM (IGA) INTERACTIVE GENETIC ALGORITHM (IGA) o A genetic algorithm (GA) can be understood as the intelligent –highly efficient- exploitation of a random search inspired by the natural evolution process o GA employs a population of individuals x j (chromosomes) and evolves this population through the application of random variation and selection operators (crossover, mutation…) Design filter solution 01 chromosome x 01 (random) Design filter solution 02 chromosome x 02 (random) crossover point parent s Design filter solution 1,1 Design filter solution 1,2 childre n 9 CONTENTS: INTRODUCTI ON THE BAYESIAN NONLOCAL FILTER INTERACTI VE GENETIC ALGORITH M IMPLEMENTA TION ISSUES & RESULTS CONCLUSIO NS Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma… k 01 ϒ 01 ξ 01 n 01 SM 01 SN 01 chromosome x 11 k 02 ϒ 02 ξ 02 n 02 SM 02 SN 02 k 01 ϒ 01 ξ 01 n 02 SM 02 SN 02 k 02 ϒ 02 ξ 02 n 01
Image of page 8
Image of page 9

You've reached the end of your free preview.

Want to read all 15 pages?

  • Fall '19
  • Supervised Evolutionary Optimization of Bayesian Nonlocal

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern

Stuck? We have tutors online 24/7 who can help you get unstuck.
A+ icon
Ask Expert Tutors You can ask You can ask You can ask (will expire )
Answers in as fast as 15 minutes
A+ icon
Ask Expert Tutors