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1 Removal of Impulse Noise in Images by Means of the Use of Support Vector Machines H. Gómez-Moreno, S. Maldonado-Bascón, F. López-Ferreras, and P. Gil-Jiménez.

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Presentation on theme: "1 Removal of Impulse Noise in Images by Means of the Use of Support Vector Machines H. Gómez-Moreno, S. Maldonado-Bascón, F. López-Ferreras, and P. Gil-Jiménez."— Presentation transcript:

1 1 Removal of Impulse Noise in Images by Means of the Use of Support Vector Machines H. Gómez-Moreno, S. Maldonado-Bascón, F. López-Ferreras, and P. Gil-Jiménez Departamento de Teoría de la Señal y Comunicaciones. Universidad de Alcalá. SPAIN IWANN 2003

2 2 Presentation of the problem n Due to a noisy transmission channel or to imperfections in the sensor that records the images, an impulse noise appears.

3 3 n There are several methods for the recuperation of this noisy images: 1) Median Filter. State of the art Reconstruction Value

4 4 State of the art Application of the 3x3 median filter

5 5 State of the art 2) SD-ROM. If the questioned pixel is far away from the central pixel it is a noisy pixel. Then it is changed. The reconstruction value is the mean value of the central pixels.

6 6 State of the art Application of the SD-ROM method

7 7 Support Vector Machines (SVMs) n We present an algorithm for impulse noise reduction based on the use of Support Vector Machines (SVMs).

8 8 Application of SVMs to noise reduction 1) We use the SVMs for two tasks: a) Classify the pixels between noisy and not noisy. b) Obtain the reconstruction value by means of the SVMs regression.

9 9 Application of SVMs to noise reduction 2) Classification. Training. n In the training the  values and the support vectors are obtained. n This training is made by minimizing the distance between the decision frontier and the data. In the non linear case it is made in the feature space (non linear transformation).

10 10 Application of SVMs to noise reduction n If the value of f(x) is positive there is noise, if it is negative there is no noise. 3) Application of the classification.

11 11 Application of SVMs to noise reduction 4) Regression. Training. Values of the original image In this case the central pixel is not used.

12 12 Application of SVMs to noise reduction 5) Regression. Application. Approximated values

13 13 Results Noisy Image 20%Reconstructed image

14 14 Results Image 50% noisyReconstructed image

15 15 Results Image 20% noisyReconstructed image 30% training

16 16 Results Image 20% noisyReconstructed image 40% training

17 17 Results PSNR results using different methods

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