This paper is published in Volume-4, Issue-3, 2018
Area
Image Processing
Author
Gurinder Singh, Pankaj Sharma, Puneet Jain
Org/Univ
Adesh Institute of Engineering and Technology, Faridkot, Punjab, India
Pub. Date
01 June, 2018
Paper ID
V4I3-1673
Publisher
Keywords
Image De-noising, Weighted average, Pixel fitness, Edge detection, Image enhancement

Citationsacebook

IEEE
Gurinder Singh, Pankaj Sharma, Puneet Jain. Hybrid technique for better noise removal to enhance edge detection, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Gurinder Singh, Pankaj Sharma, Puneet Jain (2018). Hybrid technique for better noise removal to enhance edge detection. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.

MLA
Gurinder Singh, Pankaj Sharma, Puneet Jain. "Hybrid technique for better noise removal to enhance edge detection." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.

Abstract

Edge detection technique is used to detect the edge of the image. But before edge detection the image must be free from noise. The solution for removal of image and image recovery is image de-noising models which give better solution to remove the possible pixel noise from the target image matrix. The image data is taken in the form of matrix data (2-D, 3-D or N-D), which is processed in the different dimensions with various practices in order to remove (eventually fix) the noise pixels. In this paper, the weight of the pixel is taken and covariance is calculated in the 3x3 pixel blocks. This algorithm does the cross-validation and checks the best fitness value of the pixel. Then the image is enhanced by using enhancement technique. Then the edges of the image are taken by using edge detection technique. The performance of proposed model has been estimated under various experiments. The results are found improved for all of the dataset images on the basis of PSNR (peak signal to noise ratio) and SSIM (structural similarity) based parameters.