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9 TH EUROPEAN conference on synthetic aperture radar NÜrNberg , germany

9 TH EUROPEAN conference on synthetic aperture radar NÜrNberg , germany.

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9 TH EUROPEAN conference on synthetic aperture radar NÜrNberg , germany

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  1. 9TH EUROPEAN conferenceonsyntheticaperture radar NÜrNberg, germany LUIS GÓMEZ, CRISTIAN MUNTEANU ElectronicEngineering and AutomaticDepartment,University of Las Palmas de Gran Canaria, CanaryIslands, SPAINMARIA BUEMI,JULIO BERLLES, MARTA MEJAILComputation and ImageProcessingGroupUniversity of Buenos Aires, Argentina Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma Preselection for Despeckling SAR Images

  2. CONTENTS:INTRODUCTIONTHE BAYESIAN NONLOCAL FILTER INTERACTIVE GENETIC ALGORITHMIMPLEMENTATION ISSUES & RESULTSCONCLUSIONS 2 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma…

  3. CONTENTS:INTRODUCTION THE BAYESIAN NONLOCAL FILTER INTERACTIVE GENETIC ALGORITHM IMPLEMENTATION ISSUES & RESULTS CONCLUSIONS • SAR systems generate the images by means of coherente processing of the scattered signals and…. they are susceptible to SPECKLE • SPECKLE can be useful to extract information form SAR images, but… • it reduces the efficiency of image segmentation or classification, and it makes more complex to interpret the images • There is a need for despeckeling !!! 3 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma…

  4. CONTENTS:INTRODUCTION THE BAYESIAN NONLOCAL FILTER INTERACTIVE GENETIC ALGORITHM IMPLEMENTATION ISSUES & RESULTS CONCLUSIONS • The goal for a despeckeling filter consists of suppressing the speckle while preserving all the scene features (especially edges) • Bayesian filters: • Adaptive and based on the MSE (mean squared error) • Use patch windows to estimate local statistics • The use of large patch windows are CPU consuming • Succesfully applied to B-mode ultrasound and SAR images 4 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma…

  5. CONTENTS:INTRODUCTION THE BAYESIAN NONLOCAL FILTER INTERACTIVE GENETIC ALGORITHM IMPLEMENTATION ISSUES & RESULTS CONCLUSIONS • What is our objective??????? • The major problem when designing a filter is to reach a trade-off between the output image resolution and the speckle removal • To get a desired filtered image can be improved if the filter design is supervised enough? Keep trying? coulditbebetter? SUPERVISION DATA Enough!!! Interactive Evolutionary (genetic) Algorithm (IGA) acting on filter parameters Filterparameters 5 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma…

  6. CONTENTS:INTRODUCTION THE BAYESIAN NONLOCAL FILTER INTERACTIVE GENETIC ALGORITHM IMPLEMENTATION ISSUES & RESULTS CONCLUSIONS EBNL FILTER (EnhancedBayesian Non Local Filter) • 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) : M x M local neighborhood N x N observationpatch • Under the assumption of fully developed and non-correlated speckle samples, p(v(x)|u(y)), can be estimated by the Gamma distribution N M x M N • ρ = k /√L; k ≈ 2 Filter parameters to be optimized 6 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma…

  7. CONTENTS:INTRODUCTION THE BAYESIAN NONLOCAL FILTER INTERACTIVE GENETIC ALGORITHM IMPLEMENTATION ISSUES & RESULTS CONCLUSIONS Preselection • To account for the pixel preselection, the observation patch N x N is defined as: N(x) = ε(x) ∩ N1(x) ∩ N2(x) • To eliminate correlated pixels (ϒ threshold parameter), ϒ < 1 • Pixels with an intensity value higher than Imax/2 are preselected through the sigma range mechanism y ∊ N2(x), only if v(y) ∊ (u’(x)·I1, u’(x)·I2) N x N observationpatch N M x M EBNL Filter Design parameters: N Number of Looks Uncorrelated pixels Sigma mechanism It looks like a chromosome! Max. Number of iterations M x M size patch of x neigborhood N x N size patch of observation window 7 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma…

  8. CONTENTS:INTRODUCTION THE BAYESIAN NONLOCAL FILTER INTERACTIVE GENETIC ALGORITHM IMPLEMENTATION ISSUES & RESULTS CONCLUSIONS INTERACTIVE GENETIC ALGORITHM (IGA) • A genetic algorithm (GA) can be understood as the intelligent –highly efficient- exploitation of a random search inspired by the natural evolution process • GA employs a population of individuals xj (chromosomes) and evolves this population through the application of random variation and selection operators (crossover, mutation…) chromosome x 01 (random) chromosome x 02 (random) parents Design filter solution 01 Design filter solution 02 crossover point children Design filter solution 1,2 Design filter solution 1,1 chromosome x 11 chromosome x 12 9 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma…

  9. CONTENTS:INTRODUCTION THE BAYESIAN NONLOCAL FILTER INTERACTIVE GENETIC ALGORITHM IMPLEMENTATION ISSUES & RESULTS CONCLUSIONS • Mutation (operator) chromosome x 1 (random) chromosomex 2 Mutation (random) Design filter solution 2 Design filter solution 1 pdf ϒ children parents x[]…randomly generated • From a initial filter parameters set , by applying CROSSOVER and MUTATION, a population (huge) is generated Objective function??? 10 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma…

  10. CONTENTS:INTRODUCTION THE BAYESIAN NONLOCAL FILTER INTERACTIVE GENETIC ALGORITHM IMPLEMENTATION ISSUES & RESULTS CONCLUSIONS • …We have just presented a “standard” genetic algorithm • In a IGA (interactive), the user is the evaluator… the objective function ! Not to evaluate all the solutions Randomly generated population xi,100 xi,128 xi,24 Hierarchical tree clustering Population at iteration i xi,75 xi,1 xi,6 Xi,1 Xi,2 Xi,n xi,3 xi,4 xi,12 xi,2 xi,7 xi,5 User-supervision: IMAGE EVALUATION 6.8 ?? 11 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma…

  11. CONTENTS:INTRODUCTION THE BAYESIAN NONLOCAL FILTER INTERACTIVE GENETIC ALGORITHM IMPLEMENTATION ISSUES & RESULTS CONCLUSIONS IMPLEMENTATION ISSUES • original BNL filter • original EBNL filter • the evolutionary algorithm • SAR speckle simulator (gamma distribution) MATLAB GRAPHIC INTERFACE Strong reflective scatterer: preserved 1 Look SAR phantom Edge preservation Statistical estimators Mean preservation Variance reduction Evaluate the actual image Actual filter realization (vector parameters) 12 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma…

  12. CONTENTS:INTRODUCTION THE BAYESIAN NONLOCAL FILTER INTERACTIVE GENETIC ALGORITHM IMPLEMENTATION ISSUES & RESULTS CONCLUSIONS RESULTS (i) • To validate the human-into-the loop proposed methodology, a set of several SAR images with speckle has been filtered Results for the synthetic image FOM: Pratt’s Figure of Merit, [0,1] (1 for ideal edge detection) • Noise-free image, • Imagedegradedwithsimulatedspeckle (ENL=1) • (c) EBNL filter, • (d) Optimized EBNL filter [1, 1,8, 0,75, 0,92, 5 x 5, 5 x 5] Better edge preservation and ≈ 10 times faster ! 13 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma…

  13. CONTENTS:INTRODUCTION THE BAYESIAN NONLOCAL FILTER INTERACTIVE GENETIC ALGORITHM IMPLEMENTATION ISSUES & RESULTS CONCLUSIONS RESULTS (ii) Results for the real intensity SAR images (SAR images courtesy of Terra SAR-X, Infoterra GmbH & Infoterra Servicios de Geoinformación S.A.) • (a) Original SAR image • ENBL filter • Optimized EBNL filter, USER 1: [3, 1,81, 0,72, 0,81, 3 x 3, 7 x 7] • Optimized EBNL filter, USER 2: [1, 2,30, 0,61, 0,88, 3 x 3, 9 x 9] Better variance reduction and better CPU time 13 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma…

  14. CONTENTS:INTRODUCTION THE BAYESIAN NONLOCAL FILTER INTERACTIVE GENETIC ALGORITHM IMPLEMENTATION ISSUES & RESULTS CONCLUSIONS CONCLUSIONS • We proposed an interactive easy-to-use software package based on an evolutionary algorithm to perform Enhanced Bayesian NonLocal Filtering for SAR images. • As a main difference from other methodologies, there is a direct implication of a SAR image processing user which provides: • a subjective evaluation • her/his experience • Results show that the methodology works well (better variance reduction and faster CPU execution time) and it is oriented to include either other speckle filters or new statistical models suited to the special characteristics of SAR images. 15 Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma…

  15. 9TH EUROPEAN conferenceonsyntheticaperture radar NÜrNberg, germany Supervised Evolutionary Optimization of Bayesian Nonlocal Means with Sigma Preselection for Despeckling SAR Images LUIS GÓMEZ, CRISTIAN MUNTEANU ElectronicEngineering and AutomaticDepartment,University of Las Palmas de Gran Canaria, CanaryIslands, SPAINMARIA BUEMI,JULIO BERLLES, MARTA MEJAILComputation and ImageProcessingGroupUniversity of Buenos Aires, Argentina Thanks for your attention !!

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