16-11-2012, 12:44 PM
Design of Restoration Filter for Digital Images Corrupted by Impulse Noise Using Type-2 Fuzzy Set.
[attachment=39748]
Objective:
In this project present a novel method in which the uncertainties of fuzzy membership function is modeled to reduce and the concept of this reduced uncertainties is used to detect the impulse corrupted pixels of digital images.
Technical Details:
This project presents a two step method to remove impulse noise from digital images. First the detection process will be based on the grayscale neighbourhood information and then fuzzy interval is used to remove the noise using S-shaped membership function where we find the extent of impulsiveness in more effective way. The project is organized in this way that section 2 describes the type-2 fuzzy sets.
This project presents a novel type-II fuzzy filter to remove impulse noise in an image. The filter processes impulses as type-II fuzzy sets. Type-II fuzzy sets model uncertainties more effectively than type-I fuzzy sets because the membership function for a type-I fuzzy set for a particular input is a crisp value. The proposed algorithm firstly detects impulses by considering grayscale distribution amongst neighbouring pixels and then determines the presence of impulsive pixels by comparing it with a range of threshold values using an S - shaped fuzzy membership function that is itself fuzzy. As the level of contamination varies from pixel to pixel, the modified value for the noisy pixel is calculated depending on the impulse noise present in it. The better performance of the filter is demonstrated on the basis of PSNR values calculated from the original and restored images respectively.
[attachment=39748]
Objective:
In this project present a novel method in which the uncertainties of fuzzy membership function is modeled to reduce and the concept of this reduced uncertainties is used to detect the impulse corrupted pixels of digital images.
Technical Details:
This project presents a two step method to remove impulse noise from digital images. First the detection process will be based on the grayscale neighbourhood information and then fuzzy interval is used to remove the noise using S-shaped membership function where we find the extent of impulsiveness in more effective way. The project is organized in this way that section 2 describes the type-2 fuzzy sets.
This project presents a novel type-II fuzzy filter to remove impulse noise in an image. The filter processes impulses as type-II fuzzy sets. Type-II fuzzy sets model uncertainties more effectively than type-I fuzzy sets because the membership function for a type-I fuzzy set for a particular input is a crisp value. The proposed algorithm firstly detects impulses by considering grayscale distribution amongst neighbouring pixels and then determines the presence of impulsive pixels by comparing it with a range of threshold values using an S - shaped fuzzy membership function that is itself fuzzy. As the level of contamination varies from pixel to pixel, the modified value for the noisy pixel is calculated depending on the impulse noise present in it. The better performance of the filter is demonstrated on the basis of PSNR values calculated from the original and restored images respectively.