Combining Robust Statistical and 1D Laplacian Operators Using Genetic Programming to Detect and Remove Impulse Noise from Images
2015 13th International Conference on Frontiers of Information Technology (FIT) (2015)
Dec. 14, 2015 to Dec. 16, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FIT.2015.15
In this paper, genetic programming (GP) based intelligent scheme is proposed for the denoising of digital images from impulse noise. Mixed impulse noise model which comprises a mixture of both salt & pepper, and uniform impulse noise, is considered. The proposed scheme works in two stages. First stage detects impulse noise in the image through a novel single-stage GP detector which is based on the extraction of robust statistical features and convolution of corrupted image with 1D Laplacian operators. The second stage consists of a GP based estimator that removes the noise by estimating the pixel value. This estimator approximates the pixel value by calculating the statistical features in the neighborhood of noise-free pixels. The idea of developing a single-stage detector and estimator is very effective in the removal of impulse noise. The proposed approach is tested on a variety of standard images and its comparison with other relevant techniques show that the performance of the proposed approach is better.
Noise measurement, Feature extraction, Detectors, Training, Laplace equations, Genetic programming, Optical filters
S. G. Javed, A. Majid and N. Kausar, "Combining Robust Statistical and 1D Laplacian Operators Using Genetic Programming to Detect and Remove Impulse Noise from Images," 2015 13th International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 2015, pp. 18-23.